黑马AI就业班人工智能python视频nlp机器视觉课程CV自然语言
Python深度学习与CV实战
编辑点评
课程内容丰富，理论与实践结合紧密，适合有志于AI领域发展的初学者。
⭐ 编辑推荐
本课程深入浅出地讲解了机器学习、深度学习、计算机视觉等AI核心技术，通过实战项目提升实战能力。
课程亮点
• Python深度学习实战
• 计算机视觉应用
• 机器学习算法解析
课程目录
📁 阶段4-机器学习与多场景项目实战
📁 📁 02-KNN算法
05-【重点】特征预处理_.mp4 [49.5 MB]
02-【重点】KNN算法思想_.mp4 [57.2 MB]
12-【实践】手写数字识别_.mp4 [44.9 MB]
09-【总结】内容总结_.mp4 [5.6 MB]
03-【掌握】KNN算法的API_.mp4 [33.9 MB]
01-内容回顾_.mp4 [29.5 MB]
06-【实践】鸢尾花案例_.mp4 [28.6 MB]
10-【重点】网格搜索交叉验证_.mp4 [65.2 MB]
07-【实践】特征工程_.mp4 [21.0 MB]
11-【实践】手写数字识别_.mp4 [31.5 MB]
13-【总结】内容总结_.mp4 [26.5 MB]
08-【实践】模型训练与评估_.mp4 [51.1 MB]
04-【重点】距离度量方式_.mp4 [39.2 MB]
📁 📁 08-聚类kmeans算法
08-内容总结_.mp4 [14.0 MB]
05-sc系数_.mp4 [32.0 MB]
06-ch系数_.mp4 [30.9 MB]
07-顾客群体聚类案例_.mp4 [76.0 MB]
03-Kmeans的流程案例_.mp4 [33.6 MB]
01-聚类算法_.mp4 [59.7 MB]
04-sse+肘部法_.mp4 [46.8 MB]
02.Kmeans的流程 _.mp4 [17.9 MB]
📁 📁 01-机器学习概述
02-【简介】内容简介_.mp4 [2.9 MB]
12-【重点】模型拟合_.mp4 [36.9 MB]
14-总结_.mp4 [29.3 MB]
04-【了解】人工智能应用领域_.mp4 [19.7 MB]
01-【说明】课前说明_.mp4 [20.7 MB]
09-【重点】算法分类_.mp4 [29.3 MB]
06-【总结】内容总结_.mp4 [12.9 MB]
05-【掌握】数据集描述_.mp4 [20.5 MB]
10-【重点】建模流程_.mp4 [34.8 MB]
03-【知道】AI ML DL介绍_.mp4 [28.3 MB]
11-【重点】特征工程_.mp4 [33.3 MB]
07-【掌握】算法分类_.mp4 [24.2 MB]
13-【实践】环境安装_.mp4 [18.4 MB]
08-【重点】分类与回归_.mp4 [6.3 MB]
📁 📁 09-支持向量机SVM
04-svm推导过程1_.mp4 [37.4 MB]
09.核函数总结_.mp4 [8.2 MB]
05-svm推导过程2_.mp4 [13.0 MB]
03-SVM的API_.mp4 [37.9 MB]
02.支持向量机_.mp4 [56.0 MB]
03-C参数的调整_.mp4 [15.6 MB]
08.核函数_.mp4 [78.7 MB]
07.svm推导过程4_.mp4 [18.8 MB]
01.内容回顾_.mp4 [15.1 MB]
06.svm推导过程3_.mp4 [9.4 MB]
📁 📁 03-线性回归
11-【掌握】梯度下降推导_.mp4 [28.2 MB]
10-【掌握】梯度下降简介_.mp4 [40.7 MB]
03-【实践】线性回归API的使用_.mp4 [24.8 MB]
09-【说明】正规方程损失说明_.mp4 [10.8 MB]
02-【理解】线性回归介绍_.mp4 [52.8 MB]
01-【回顾】内容回顾_.mp4 [27.2 MB]
15-【实践】波士顿房价案例_.mp4 [68.6 MB]
13-【总结】内容总结_.mp4 [20.6 MB]
04-【思想】线性回归的思想_.mp4 [67.8 MB]
05-【复习】导数_.mp4 [29.4 MB]
08-【理解】正规方程的求解过程_.mp4 [36.0 MB]
14-【理解】模型评估方法_.mp4 [36.8 MB]
17-【重点】模型拟合_.mp4 [72.9 MB]
16-【掌握】拟合问题_.mp4 [81.6 MB]
06-【复习】矩阵_.mp4 [68.6 MB]
12-【理解】梯度下降算法案例+分类_.mp4 [59.4 MB]
07-【理解】正规方法法_.mp4 [32.7 MB]
📁 📁 06-集成学习
10-【重点】XGBoost_.mp4 [106.3 MB]
14-【总结】集成学习_.mp4 [1.8 MB]
12.xgboost案例_.mp4 [87.5 MB]
13-【案例】xgboost_.mp4 [4.6 MB]
04-【重点】adaboost_.mp4 [42.6 MB]
05-【理解】案例_.mp4 [41.8 MB]
11.Xgboost的思想_.mp4 [11.2 MB]
02-【重点】随机森林_.mp4 [23.9 MB]
07-【回顾】内容回顾_.mp4 [20.1 MB]
09-【实践】GBDT案例_.mp4 [13.5 MB]
07-adaboost案例_.mp4 [11.7 MB]
08-【重点】GBDT_.mp4 [78.2 MB]
03-【实践】泰坦尼克号实践_.mp4 [30.9 MB]
06-葡萄酒案例_.mp4 [25.9 MB]
01-集成学习简介_.mp4 [58.9 MB]
📁 📁 05-决策树
06-【总结】内容总结_.mp4 [31.6 MB]
08-【回顾】内容回顾_.mp4 [95.4 MB]
09-【理解】决策树对比_.mp4 [77.4 MB]
10-【理解】回归树_.mp4 [48.7 MB]
02-【理解】ID3树的推导_.mp4 [57.5 MB]
07-内容回顾_.mp4 [16.1 MB]
04-【练习】练习_.mp4 [8.5 MB]
03-【推导】ID3树案例_.mp4 [41.1 MB]
11-【理解】剪枝方法_.mp4 [18.1 MB]
05-【理解】C4.5树_.mp4 [37.0 MB]
01-【理解】决策树简介_.mp4 [24.4 MB]
📁 📁 04-逻辑回归
08-【重点】分类评估指标_.mp4 [49.2 MB]
06-【总结】_.mp4 [19.5 MB]
10-【了解】ROC和AUC_.mp4 [63.3 MB]
04-【理解】损失函数介绍_.mp4 [34.7 MB]
11.电信客户流失案例_.mp4 [88.0 MB]
05-【实践】癌症分类案例_.mp4 [44.2 MB]
07.线性回归回顾_.mp4 [34.4 MB]
01-【知道】逻辑回归的应用场景_.mp4 [49.4 MB]
02-【知道】数学知识_.mp4 [32.2 MB]
09-【掌握】精确率，召回率和法score_.mp4 [42.2 MB]
03-【理解】逻辑回归思想_.mp4 [18.7 MB]
📁 📁 07-朴素贝叶斯和特征降维
02-【实践】情感分析案例_.mp4 [101.7 MB]
06-相关系数法_.mp4 [52.3 MB]
01-【理解】贝叶斯原理_.mp4 [53.6 MB]
03-集成学习思想_.mp4 [16.5 MB]
04-特征降维+低方差过滤法_.mp4 [23.8 MB]
05-PCA方法_.mp4 [21.1 MB]
📁 阶段16-串讲 赠品
📁 📁 AI模型部署-17期
📁 📁 3 练习
模型部署练习.zip [46.7 MB]
📁 📁 2 课件
模型部署.zip [1.0 MB]
📁 📁 01-加密视频
18-容器部署-镜像操作_.wmv [50.9 MB]
15-服务接口-服务封装_.wmv [97.0 MB]
03-模型训练-数据格式转换_.wmv [70.8 MB]
16-容器部署-介绍安装_.wmv [62.1 MB]
09-服务接口-Flask作用_.wmv [30.8 MB]
02-模型训练-数据集介绍_.wmv [82.3 MB]
10-服务接口-Flask-Hello-World-1_.wmv [35.7 MB]
07-模型训练-邮件模型评估_.wmv [82.1 MB]
19-容器部署-容器操作_.wmv [35.8 MB]
08-模型训练-邮件模型预测_.wmv [41.5 MB]
14-服务接口-表单扩展_.wmv [40.4 MB]
11-服务接口-Flask-Hello-World-2_.wmv [37.0 MB]
20-容器部署-手动构建镜像_.wmv [107.2 MB]
12-服务接口-创建表单_.wmv [72.2 MB]
13-服务接口-处理表单_.wmv [33.8 MB]
05-模型训练-文本特征提取_.wmv [65.9 MB]
22-模型部署回顾_.wmv [34.3 MB]
17-容器部署-快速入门_.wmv [83.4 MB]
04-模型训练-邮件数据清洗_.wmv [227.7 MB]
21-容器部署-自动构建镜像_.wmv [67.9 MB]
01-模型部署内容概述_.wmv [13.6 MB]
06-模型训练-邮件模型训练_.wmv [18.2 MB]
📁 📁 李刚#AI关系抽取项目#17期
📁 📁 day04
📁 📁 03-代码
📁 📁 relationship_extract
📁 📁 codes
📁 📁 templates
chat.html [457.0 B]
index.html [618.0 B]
📁 📁 utils
📁 📁 __pycache__
data_loader.cpython-36.pyc [1.6 KB]
process.cpython-36.pyc [5.0 KB]
ai16_process.cpython-36.pyc [5.2 KB]
ai16_data_loader.cpython-36.pyc [1.9 KB]
__init__.cpython-36.pyc [239.0 B]
process.py [8.7 KB]
ai16_process.py [7.9 KB]
ai16_data_loader.py [2.2 KB]
data_loader.py [2.0 KB]
__init__.py [121.0 B]
📁 📁 __pycache__
__init__.cpython-36.pyc [186.0 B]
config.cpython-36.pyc [1.8 KB]
config.cpython-39.pyc [1.2 KB]
predict.cpython-36.pyc [2.7 KB]
ai16_config.cpython-36.pyc [1.8 KB]
📁 📁 model
📁 📁 __pycache__
CasrelModel.cpython-36.pyc [5.1 KB]
__init__.cpython-36.pyc [157.0 B]
ai16_CasrelModel.cpython-36.pyc [4.7 KB]
__init__.cpython-38.pyc [215.0 B]
CasrelModel.cpython-38.pyc [4.8 KB]
__init__.py [69.0 B]
CasrelModel.py [7.9 KB]
ai16_CasrelModel.py [6.1 KB]
predict.py [4.1 KB]
map_display.py [11.0 KB]
ai16_config.py [1.8 KB]
config.py [1.8 KB]
test.py [3.2 KB]
flask_test.py [588.0 B]
__init__.py
aaaa.py [814.0 B]
flask_web.py [1.1 KB]
train.py [5.9 KB]
ai16_trian.py [6.1 KB]
new_flask.py [871.0 B]
📁 📁 bert-base-chinese
tokenizer_config.json [29.0 B]
config.json [624.0 B]
tokenizer.json [262.6 KB]
README.md [21.0 B]
vocab.txt [107.0 KB]
pytorch_model.bin [392.5 MB]
📁 📁 data
predict_spo.json [2.6 MB]
dev.json [4.0 MB]
rel_type.json [3.4 KB]
train.json [20.1 MB]
test.json [5.4 MB]
__init__.py [69.0 B]
relation.json [364.0 B]
📁 📁 save_model
best_f1.pth [390.3 MB]
__init__.py
adaa.py [174.0 B]
📁 📁 01-加密视频
15-test函数代码实现_.wmv [176.9 MB]
14-train函数代码的实现_.wmv [106.0 MB]
06-Casrel模型代码搭建实现_.wmv [66.2 MB]
02-Casrel模型init函数实现_.wmv [48.3 MB]
13--train函数讲解_.wmv [34.9 MB]
11-extract_obj_and_rel函数的实现_.wmv [22.7 MB]
01-内容回顾_.wmv [46.1 MB]
08-extract_sub函数代码分析_.wmv [221.3 MB]
03-Casrel模型get_encode_result和get_subs代码实现_.wmv [151.2 MB]
10-extract_obj_and_relation函数的分析_.wmv [29.6 MB]
07-load_model函数代码实现_.wmv [139.2 MB]
09-extract_sub代码实现_.wmv [18.6 MB]
05-Casrel模型forward函数实现_.wmv [36.0 MB]
04-Casrel模型客实体识别代码_.wmv [138.3 MB]
12-train_eopch和modle2dev函数讲解_.wmv [228.5 MB]
📁 📁 02-笔记
📁 📁 第五章内容笔记
05-03-Casrel数据处理介绍.md [18.1 KB]
05-02-Casrel模型架构.md [3.4 KB]
05-04-Casrel模型代码实现与训练.md [22.0 KB]
05-01-joint方法介绍.md [1.8 KB]
📁 📁 img
4-1-2.png [57.7 KB]
pr.png [17.4 KB]
4-4-1.png [1.2 MB]
5-2-22.jpeg [154.5 KB]
1-2-1.png [94.2 KB]
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5-2-4.png [25.2 KB]
4-3-1.png [100.9 KB]
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5-2-11.jpeg [146.6 KB]
5-2-28.jpeg [93.0 KB]
5-2-18.jpeg [157.8 KB]
1-1-3.png [476.1 KB]
3-3-1.png [86.7 KB]
3-4-2.png [72.7 KB]
image-20230111212028757.png [95.0 KB]
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5-2-29.jpeg [91.6 KB]
image-20230108233852229.png [412.7 KB]
1-1-4.png [712.2 KB]
5-2-2.png [152.2 KB]
5-2-26.jpeg [91.4 KB]
5-2-1.png [144.4 KB]
yule_01.png [207.3 KB]
5-2-5.png [137.1 KB]
5-2-13.jpeg [93.0 KB]
1-2-2.png [19.8 KB]
3-4-1.png [192.2 KB]
AI.jpg [46.0 KB]
5-2-12.jpeg [104.9 KB]
5-2-16.jpeg [105.5 KB]
5-2-23.jpeg [157.6 KB]
1-2-3.png [16.4 KB]
4-2-3.png [299.7 KB]
neo4j_01.png [177.1 KB]
4-2-2.png [296.0 KB]
5-2-7.png [145.0 KB]
5-2-31.jpeg [84.5 KB]
5-2-27.jpeg [96.5 KB]
5-2-25.jpeg [87.0 KB]
1-1-2.png [1.3 MB]
5-2-10.jpeg [111.2 KB]
5-2-24.jpeg [185.8 KB]
5-2-9.png [144.6 KB]
4-1-1.png [149.2 KB]
image-20230112002118366.png [61.2 KB]
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image-20230108224144400.png [1.3 MB]
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4-2-1.png [379.0 KB]
5-2-3.png [117.6 KB]
5-2-6.png [153.3 KB]
📁 📁 day03
📁 📁 02-笔记
📁 📁 img
5-2-15.jpeg [99.3 KB]
4-2-3.png [299.7 KB]
5-2-6.png [153.3 KB]
5-2-10.jpeg [111.2 KB]
5-2-18.jpeg [157.8 KB]
1-1-2.png [1.3 MB]
neo4j_01.png [177.1 KB]
3-2-1.png [96.6 KB]
5-2-30.jpeg [84.5 KB]
image-20230108224144400.png [1.3 MB]
5-2-25.jpeg [87.0 KB]
3-3-1.png [86.7 KB]
yule_01.png [207.3 KB]
image-20230112002118366.png [61.2 KB]
5-2-5.png [137.1 KB]
1-1-1.png [65.2 KB]
image-20230108233852229.png [412.7 KB]
5-2-13.jpeg [93.0 KB]
5-2-16.jpeg [105.5 KB]
5-2-7.png [145.0 KB]
1-2-3.png [16.4 KB]
5-2-28.jpeg [93.0 KB]
1-2-4.png [16.2 KB]
1-1-3.png [476.1 KB]
1-2-1.png [94.2 KB]
5-2-17.png [63.7 KB]
3-4-2.png [72.7 KB]
5-2-31.jpeg [84.5 KB]
pr.png [17.4 KB]
5-2-23.jpeg [157.6 KB]
5-2-29.jpeg [91.6 KB]
4-4-1.png [1.2 MB]
4-2-4.png [6.8 KB]
5-2-14.jpeg [104.7 KB]
5-2-3.png [117.6 KB]
5-2-9.png [144.6 KB]
5-2-11.jpeg [146.6 KB]
5-2-26.jpeg [91.4 KB]
4-1-2.png [57.7 KB]
4-2-2.png [296.0 KB]
5-2-21.jpeg [157.5 KB]
1-2-2.png [19.8 KB]
5-2-27.jpeg [96.5 KB]
5-2-1.png [144.4 KB]
5-2-2.png [152.2 KB]
5-2-8.png [104.1 KB]
AI.jpg [46.0 KB]
image-20230111212028757.png [95.0 KB]
1689568362538.png [17.4 KB]
1-1-4.png [712.2 KB]
5-2-12.jpeg [104.9 KB]
4-3-1.png [100.9 KB]
5-2-4.png [25.2 KB]
4-2-5.png [75.2 KB]
4-1-1.png [149.2 KB]
3-4-1.png [192.2 KB]
5-2-22.jpeg [154.5 KB]
5-2-24.jpeg [185.8 KB]
4-2-1.png [379.0 KB]
📁 📁 第四章笔记
03-BILSTM+Attention模型数据预处理.md [12.9 KB]
02-BiLSTM+Attention模型介绍.md [4.6 KB]
01-pipeline方法介绍.md [1.7 KB]
04-BILSTM+Attention模型实现与训练.md [8.4 KB]
📁 📁 03-代码
📁 📁 Bilstm_Attention_RE
📁 📁 utils
📁 📁 __pycache__
ai16_data_loader.cpython-36.pyc [3.0 KB]
data_loader.cpython-36.pyc [2.6 KB]
process.cpython-36.pyc [3.1 KB]
__init__.cpython-36.pyc [140.0 B]
ai16_process.cpython-36.pyc [3.6 KB]
data_loader.py [2.5 KB]
ai16_data_loader.py [3.2 KB]
process.py [4.2 KB]
__init__.py [69.0 B]
ai16_process.py [4.2 KB]
📁 📁 data
relation2id.txt [44.0 B]
test.txt [906.6 KB]
train.txt [2.7 MB]
📁 📁 .idea
deployment.xml [371.0 B]
modules.xml [285.0 B]
misc.xml [301.0 B]
bilstm_crf_re.iml [610.0 B]
workspace.xml [31.6 KB]
📁 📁 save_model
20230228_new_model_20.bin [2.7 MB]
20230228_new_model_10.bin [2.7 MB]
20230228_new_model_30.bin [2.7 MB]
20230228_new_model_0.bin [2.7 MB]
20230228_new_model_40.bin [2.7 MB]
📁 📁 model
📁 📁 __pycache__
bilstm_atten.cpython-36.pyc [2.2 KB]
ai16_bilstm_atten.cpython-36.pyc [2.3 KB]
ai16_bilstm_atten.py [4.1 KB]
bilstm_atten.py [3.8 KB]
📁 📁 __pycache__
config.cpython-36.pyc [1.1 KB]
ai16_config.cpython-36.pyc [1.2 KB]
📁 📁 img
loss1.png [197.9 KB]
config.py [1.4 KB]
ai16_config.py [1.2 KB]
ai16_predict.py [2.0 KB]
ai16_train.py [2.5 KB]
train.py [2.5 KB]
__init__.py [70.0 B]
predict.py [1.9 KB]
📁 📁 01-加密视频
11-create_label代码实现_.wmv [145.6 MB]
14-Dataset类代码实现_.wmv [36.0 MB]
03-Joint方法优缺点_.wmv [4.9 MB]
08-第一个数据处理函数find_head_index实现_.wmv [27.0 MB]
13--collate_fn自定义函数实现_.wmv [60.9 MB]
15-Dataloader类代码分析和实现_.wmv [48.5 MB]
04-Casrel模型架构讲解_.wmv [61.5 MB]
06-Casrel数据集介绍_.wmv [39.3 MB]
01-知识回顾_.wmv [34.8 MB]
10-create_label代码思想_.wmv [83.8 MB]
09-数据预处理函数creat_label思想_.wmv [358.6 MB]
05-Casrel模型架构_.wmv [16.5 MB]
02-Joint方法原理介绍_.wmv [37.1 MB]
12-collate_fn自定义函数代码分析_.wmv [48.2 MB]
07-Config类代码的实现_.wmv [69.7 MB]
📁 📁 day05
📁 📁 02-笔记
📁 📁 img
5-2-8.png [104.1 KB]
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📁 📁 01-加密视频
10-py2neo操作neo4j结尾_.wmv [34.6 MB]
06-Neo4j图数据库创建关系_.wmv [21.5 MB]
05-Neo4j图数据库创建节点_.wmv [76.3 MB]
07-Neo4j图数据库查询节点_.wmv [72.7 MB]
04-Neo4j图数据库安装介绍_.wmv [44.2 MB]
11-Neo4j图数据库准备数据_.wmv [149.0 MB]
02-API接口的制作_.wmv [54.8 MB]
03-API接口的测试_.wmv [64.1 MB]
13-将所有预测的SPO三元组数据导入Neo4j_.wmv [68.3 MB]
12-Neo4j创建节点函数_.wmv [50.5 MB]
08-关系查询_.wmv [115.5 MB]
01-知识回顾_.wmv [43.0 MB]
09-节点和关系的删除_.wmv [225.1 MB]
📁 📁 03-代码
📁 📁 day01
📁 📁 03-课件
03-基于规则方法实现关系抽取.pptx [1.5 MB]
04-基于Pipeline方法实现关系抽取.pptx [4.8 MB]
01-课程简介.pptx [6.0 MB]
02-项目背景介绍.pptx [5.0 MB]
📁 📁 02-笔记
📁 📁 img
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4-2-4.png [6.8 KB]
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AI.jpg [46.0 KB]
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5-2-31.jpeg [84.5 KB]
5-2-25.jpeg [87.0 KB]
5-2-11.jpeg [146.6 KB]
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image-20230108233852229.png [412.7 KB]
pr.png [17.4 KB]
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1-1-3.png [476.1 KB]
5-2-4.png [25.2 KB]
5-2-14.jpeg [104.7 KB]
5-2-18.jpeg [157.8 KB]
neo4j_01.png [177.1 KB]
5-2-15.jpeg [99.3 KB]
4-2-1.png [379.0 KB]
5-2-26.jpeg [91.4 KB]
1-2-2.png [19.8 KB]
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1-2-1.png [94.2 KB]
02-关系抽取项目背景介绍.md [7.3 KB]
01-课程简介.md [1.0 KB]
03-规则方法介绍.md [3.7 KB]
📁 📁 01-加密视频
08-规则实现关系抽取代码分析_.wmv [42.8 MB]
04-关系抽取任务的特点_.wmv [27.8 MB]
05-关系抽取任务的指标和问题_.wmv [43.4 MB]
10-Pipeline方式原理介绍_.wmv [52.0 MB]
14--数据预处理函数sent_padding实现_.wmv [95.1 MB]
09-规则实现关系抽取总结mp4_.wmv [13.9 MB]
06--关系抽取第一小结_.wmv [22.9 MB]
11-BiLSTM+Attention模型架构分析_.wmv [14.9 MB]
07--规则进行关系抽取任务介绍_.wmv [41.7 MB]
02-关系抽取项目背景介绍_.wmv [42.5 MB]
03-关系抽取其业务他应用场景_.wmv [25.1 MB]
12--BiLSTM+Attention模型配置文件_.wmv [66.3 MB]
13-BILSTM+Attention数据预处理函数配置_.wmv [28.7 MB]
01-关系抽取项目简介_.wmv [51.1 MB]
📁 📁 04-代码
ai16_config.py [1.2 KB]
ai16_process.py [1.1 KB]
📁 📁 day02
📁 📁 03-课件
04-基于Pipeline方法实现关系抽取.pptx [4.8 MB]
📁 📁 04-代码
ai16_config.py [1.2 KB]
ai16_process.py [1.1 KB]
📁 📁 01-加密视频
18--模型预测代码的实现_.wmv [79.1 MB]
13--forward函数代码分析_.wmv [70.8 MB]
16--模型训练_.wmv [86.6 MB]
03-数据预处理pos函数和pos_padding函数的实现_.wmv [33.2 MB]
09--Dataset类和Dataloader类实现_.wmv [40.5 MB]
14--foward函数代码实现_.wmv [48.3 MB]
07--DataSet类的实现_.wmv [26.2 MB]
06--get_word_id函数实现_.wmv [55.5 MB]
05-get_txt_data函数代码实现_.wmv [49.8 MB]
10--BiLSTM+Attention模型类init方法讲解_.wmv [32.4 MB]
15--模型数据形状结果分析_.wmv [50.5 MB]
08--自定义函数collate_fn函数的实现_.wmv [76.3 MB]
01-昨日视频回顾_.wmv [80.5 MB]
11-init方法代码实现_.wmv [71.5 MB]
17--预测代码分析_.wmv [24.0 MB]
04-数据预处理函数get_txt_data讲解_.wmv [42.8 MB]
12--初始化参数方法和attention方法实现_.wmv [32.1 MB]
19--今日内容总结_.wmv [31.2 MB]
02-数据预处理Pos函数讲解_.wmv [10.2 MB]
📁 📁 02-笔记
📁 阶段13-计算机视觉
📁 📁 此部分为赠送教程-CV
📁 📁 Opencv视频教程
📁 📁 02 OpenCV特征提取与检测实战视频课程 （课件+源码）
19-Haar特征.ts [63.6 MB]
08-亚像素级别角点检测.ts [118.1 MB]
14-HOG特征检测-02.ts [97.2 MB]
26-级联分类器 – 人脸检测.ts [103.2 MB]
16-LBP(Local Binary Patterns)特征-02.ts [71.3 MB]
10-SURF特征检测-02.ts [79.3 MB]
07-自定义角点检测器-02.ts [87.5 MB]
03-Harris角点检测-01.ts [84.5 MB]
24-AKAZE局部匹配-02.ts [108.5 MB]
25-Brisk特征检测与匹配.ts [102.8 MB]
15-LBP(Local Binary Patterns)特征-01.ts [75.5 MB]
05-Shi-Tomasi角点检测.ts [130.4 MB]
04-Harris角点检测-02.ts [89.0 MB]
22-平面对象识别.ts [129.2 MB]
18-积分图计算.ts [68.9 MB]
13-HOG特征检测-01.ts [71.9 MB]
09-SURF特征检测-01.ts [83.2 MB]
课程配套PDF.zip [11.3 MB]
02-OpenCV3.1.0编译.ts [120.2 MB]
06-自定义角点检测器-01.ts [129.0 MB]
12-SIFT特征检测-02.ts [61.0 MB]
21-FLANN特征匹配.ts [93.9 MB]
20-特征描述子.ts [71.9 MB]
11-SIFT特征检测-01.ts [104.1 MB]
01-概述.ts [30.0 MB]
23-AKAZE局部匹配-01.ts [84.3 MB]
17-LBP(Local Binary Patterns)特征-03.ts [150.9 MB]
课程配套源代码.zip [13.5 KB]
📁 📁 11 OpenCV & FFmpeg & Qt C++视频编辑器实战开发
📁 📁 03 OpenCV图像处理
022 图像尺寸调整双线程插值算法讲解和性能测试~1.mp4 [16.0 MB]
021 调用opencv的resize使用近邻算法并与自定义算法比较~1.mp4 [21.1 MB]
015 通过ROI感兴趣区域来裁剪图像~1.mp4 [9.8 MB]
018 通过OpenCV阈值函数threshold实现图像的二值化~1.mp4 [12.9 MB]
016 RGBYUVGRAY像素格式介绍opencv像素格式转换cvtColor接口讲解~1.mp4 [7.3 MB]
026 通过ROI实现图像并排合并~1.mp4 [20.6 MB]
019 通过对Mat遍历修改图像亮度和对比度与convertTo性能对比~1.mp4 [22.1 MB]
025 图像旋转和镜像~1.mp4 [8.7 MB]
017 手动实现转换灰度图并与opencv提供的函数做性能对比~1.mp4 [23.0 MB]
023 高斯金字塔和拉普拉斯金字塔调整图像尺寸详解~1.mp4 [15.7 MB]
020 图像尺寸调整算法介绍并手动实现近邻算法~1.mp4 [10.9 MB]
024 实现两幅图像混合blending~1.mp4 [11.1 MB]
📁 📁 04 FFMpeg工具处理音频
027 使用ffmpeg工具实现音频抽取剪切和与视频合并~1.mp4 [17.3 MB]
📁 📁 06 XVideoEdit视频编辑器实战
📁 📁 attached_files
📁 📁 051 调整视频亮度对比度3完成界视频结果显示
3XVideoEdit.zip [15.2 KB]
📁 📁 068 完成了视频剪辑包含音频剪辑
14XVideoEdit-Linux.zip [19.0 KB]
📁 📁 059 通过ROI裁剪视频画面
8XVideoEdit.zip [82.9 KB]
📁 📁 064 两路视频的横向合并为一个视频
12XVideoEdit.zip [104.8 KB]
📁 📁 061 视频添加水印
10XVideoEdit.zip [45.2 KB]
📁 📁 062 视频融合1-完成了打开第二个视频源
11XVideoEdit-blend.zip [45.7 KB]
📁 📁 058 通过图像金字塔调整视频尺寸
7XVideoEdit.zip [44.6 KB]
📁 📁 042 完成视频编辑器播放界面并完成绘制视频widget重载
1XVideoEdit.zip [11.2 KB]
📁 📁 060 转换为灰度图视频并导出
9XVideoEdit.zip [44.8 KB]
📁 📁 048 通过QSlider滑动条拖动完成视频播放位置跳转
2XVideoEdit.zip [12.3 KB]
📁 📁 052 视频的导出1接口调用搭建和界面实现完成
4XVideoEdit.zip [15.2 KB]
📁 📁 056 视频上下左右镜像
6XVideoEdit.zip [44.0 KB]
📁 📁 055 视频图像旋转并导出
5XVideoEdit.zip [81.2 KB]
📁 📁 065 音频类的抽取接口开发和测试
13XVideoEdit.zip [66.7 KB]
063 视频融合2-完成了融合和导出~1.mp4 [34.2 MB]
047 视频播放器进度条QSlider显示播放进度~1.mp4 [25.8 MB]
042 完成视频编辑器播放界面并完成绘制视频widget重载~1.mp4 [20.0 MB]
051 调整视频亮度对比度3完成界视频结果显示~1.mp4 [36.7 MB]
053 视频导出2功能实现~1_吾爱程序猿论坛用户分享.mp4 [44.0 MB]
067 完成了视频的开始结束位置剪辑音频未处理~1.mp4 [41.9 MB]
045 使用opencv读取并解码视频通过信号槽机制发出绘制信号~1.mp4 [20.5 MB]
054 完成播放暂停并使用qss设置播放暂停按钮样式效果~1.mp4 [28.3 MB]
038 编辑器的需求分析和最终实现的功能介绍~1.mp4 [18.7 MB]
061 视频添加水印~1_吾爱程序猿论坛用户分享.mp4 [49.1 MB]
064 两路视频的横向合并为一个视频~1.mp4 [39.7 MB]
058 通过图像金字塔调整视频尺寸~1.mp4 [33.1 MB]
040 基于QT系统界面设计详解~1.mp4 [6.8 MB]
062 视频融合1-完成了打开第二个视频源~1.mp4 [30.6 MB]
068 完成了视频剪辑包含音频剪辑~1.mp4 [29.6 MB]
050 调整视频亮度对比度2完成XFilter类~1.mp4 [23.2 MB]
060 转换为灰度图视频并导出~1.mp4 [39.0 MB]
066 完成视频中音频的的合并导出~1.mp4 [33.5 MB]
048 通过QSlider滑动条拖动完成视频播放位置跳转~1.mp4 [29.2 MB]
049 调整视频亮度对比度1完成XImagePro类~1.mp4 [19.5 MB]
041 实战项目环境搭建项目创建和配置~1.mp4 [13.4 MB]
065 音频类的抽取接口开发和测试~1.mp4 [22.4 MB]
056 视频上下左右镜像~1_吾爱程序猿论坛用户分享.mp4 [13.8 MB]
044 通过qt界面打开外部视频并完成打开失败的界面提示~1.mp4 [33.0 MB]
052 视频的导出1接口调用搭建和界面实现完成~1.mp4 [26.9 MB]
059 通过ROI裁剪视频画面~1.mp4 [35.3 MB]
043 详解通过qss完成界面风格设置设置按钮圆角和渐变颜色~1.mp4 [8.8 MB]
039 项目类图介绍和类功能讲解~1.mp4 [6.8 MB]
057 调整视频尺寸并导出~1.mp4 [23.8 MB]
055 视频图像旋转并导出~1_吾爱程序猿论坛用户分享.mp4 [29.9 MB]
046 解码并使用播放视频分析并解决QImage图像数据不连续问题~1.mp4 [48.6 MB]
📁 📁 02 OpenCV核心类型 Mat
📁 📁 attached_files
📁 📁 007 OpenCV Mat类型分析源码介绍空间创建和释放
-src-1.zip [9.7 MB]
010 遍历不连续的OpenCV Mat空间~1.mp4 [8.1 MB]
009 使用opencv接口实现运行记时函数用来分析执行效率~1.mp4 [11.9 MB]
013 通过迭代器遍历Mat并总结遍历方法~1.mp4 [7.5 MB]
014 QT自定义opengl的Widget绘制Mat~1.mp4 [27.7 MB]
007 OpenCV Mat类型分析源码介绍空间创建和释放~1.mp4 [13.3 MB]
008 遍历和修改连续的OpenCV Mat图像空间~1.mp4 [14.5 MB]
011 通过OpenCV ptr模板函数遍历Mat并测试其性能~1.mp4 [11.6 MB]
012 通过OpenCV at函数遍历Mat并捕获异常~1.mp4 [11.7 MB]
📁 📁 05 OpenCV视频IO接口
033 获取视频和相机的属性并分析获取视频属性的源码~1.mp4 [19.3 MB]
030 VideoCapture release关闭和空间释放源码分析~1.mp4 [5.6 MB]
035 通过VideoWrite的open创建视频文件并分析源码~1.mp4 [25.5 MB]
028 OpenCV VideoCapture打开摄像头接口讲解和源码分析~1.mp4 [10.1 MB]
031 OpenCV read读取一帧视频接口讲解和源码分析~1.mp4 [12.3 MB]
034 使用opencv实现视频播放位置跳转~1.mp4 [14.0 MB]
036 通过VideoWrite的write写入视频文件并分析源码~1.mp4 [14.7 MB]
037 以h264格式录制并预览摄像机视频代码演示~1.mp4 [19.0 MB]
032 使用OpenCV VideoCapture播放视频示例~1.mp4 [18.9 MB]
029 OpenCV VideoCapture打开视频流接口讲解和源码分析~1.mp4 [12.2 MB]
📁 📁 01 介绍
📁 📁 attached_files
📁 📁 006 windows 上创建opencv示例项目编译并执行
01-windows-linux-1.zip [47.2 MB]
📁 📁 002 opencv源码在windows下载编译安装
opencv3.2Linux.txt.zip [1023.0 B]
002 opencv源码在windows下载编译安装~1.mp4 [10.9 MB]
004 windows 上创建opencv示例项目编译并执行~1.mp4 [16.2 MB]
001 介绍~1.mp4 [22.0 MB]
006 windows 上创建opencv示例项目编译并执行~1.mp4 [17.4 MB]
005 ubuntu上创建opencv示例项目makefile编译并执行~1.mp4 [8.9 MB]
003 Ubuntu下编译opencv源码~1.mp4 [14.8 MB]
📁 📁 《OpenCV3编程入门》书本配套源代码
OpenCV3编程入门.pdf [82.3 MB]
【OpenCV3版】《OpenCV3编程入门》书本配套源代码.rar [24.3 MB]
📁 📁 05 OpenCV图像分割实战视频教程 （课件+源码）
10-分水岭分割方法-图像分割.ts [120.7 MB]
课程配套PDF.zip [3.0 MB]
04-KMeans方法-图像分割.ts [99.6 MB]
16-案例实战一绿幕背景视频抠图.ts [94.7 MB]
09-分水岭分割方法-对象分离与计数02.ts [110.4 MB]
15-案例实战一绿幕背景视频抠图-01.ts [97.8 MB]
02-KMeans方法-原理.ts [78.8 MB]
11-Grabcut原理与演示应用-原理.ts [110.5 MB]
08-分水岭分割方法-对象分离与计数01.ts [131.7 MB]
05-高斯混合模型(GMM)方法-原理与数据聚类.ts [139.0 MB]
课程配套代码与图片.zip [10.6 MB]
07-分水岭分割方法-原理.ts [82.9 MB]
03-KMeans方法-数据聚类.ts [80.9 MB]
12-Grabcut原理与演示应用-代码演示.ts [113.8 MB]
01-概述.ts [41.2 MB]
06-高斯混合模型(GMM)方法-图像分割.ts [130.8 MB]
14-案例实战一证件照背景替换.ts [124.5 MB]
13-案例实战一证件照背景替换-01.ts [72.3 MB]
📁 📁 09 14个常用OpenCV+C++图像处理
📁 📁 04 OpenCV级联分类器训练与使用实战教程课程 （课件+源码）
07-视频中人脸检测与眼睛跟踪-01.ts [157.9 MB]
08-视频中人脸检测与眼睛跟踪-02.ts [118.8 MB]
11-HAAR_LBP级联分类器训练-01.ts [142.5 MB]
09-视频中人脸检测与眼睛跟踪-03.ts [115.7 MB]
课程配套源代码.zip [40.3 MB]
10-HAAR级联数据文件结构与精简.ts [118.5 MB]
04-Haar与LBP级联分类器使用-01.ts [154.8 MB]
05-Haar与LBP级联分类器使用-02.ts [73.5 MB]
01-概述.ts [31.2 MB]
课程配套PDF.zip [3.2 MB]
02-Haar与LBP级联分类器原理介绍-01.ts [128.7 MB]
12-HAAR_LBP级联分类器训练-02.ts [98.6 MB]
03-Haar与LBP级联分类器原理介绍-02.ts [105.5 MB]
13-HAAR_LBP级联分类器训练-03.ts [120.1 MB]
06-HAAR猫脸检测.ts [124.1 MB]
📁 📁 08 人工智能之OpenCV人脸识别案例实战视频教程 （课件+源码）
01-概述与环境准备.ts [47.7 MB]
课程配套PDF.zip [2.5 MB]
06-人脸识别算法之EigenFace-01.ts [137.7 MB]
02-均值方差与协方差 协方差矩阵.ts [121.9 MB]
09-人脸识别算法之LBPH.ts [92.3 MB]
11-案例-实时人脸识别应用开发-02.ts [136.4 MB]
04-PCA原理与应用-01.ts [105.1 MB]
课程配套源代码.zip [3.6 MB]
10-案例-实时人脸识别应用开发-01.ts [120.6 MB]
08-人脸识别算法之FisherFace.ts [100.2 MB]
07-人脸识别算法之EigenFace-02.ts [134.8 MB]
03-特征值与特征向量.ts [90.7 MB]
05-PCA原理与应用-02.ts [161.1 MB]
📁 📁 13 附赠1：Opencv资料
图像处理、分析与机器视觉（第三版）英文版.pdf [28.0 MB]
A_Computational_Approach_to_Edge_Detection-sz4.pdf [6.3 MB]
机器视觉-张广军.pdf [48.8 MB]
视觉计算理论.pdf [21.3 MB]
opencv2计算机视觉编程手册( 扫描版1-35页).pdf [68.3 MB]
机器视觉测量技术.pdf [2.8 MB]
Learning OpenCV 2nd Early Release.pdf [10.9 MB]
图像处理分析与机器视觉(第二版)中译.pdf [41.0 MB]
图像处理与计算机视觉算法及应用 原书第2版 [（美）帕科尔著][清华大学出版社][2012.05][388页]sample.pdf [6.2 MB]
基于OpenCV的计算机视觉技术实现.pdf [186.1 MB]
OpenCV2ComputerVisionApplicationProgrammingCookbookCode.zip [116.7 KB]
计算机视觉——算法与应用.pdf.pdf [48.4 MB]
学习opencv书——源代码.zip [20.2 MB]
OpenCV.2.Computer.Vision.Application.Programming.Cookbook.pdf [6.8 MB]
图像处理、分析与机器视觉（第三版）.pdf [76.5 MB]
机器视觉算法与应用.pdf [109.2 MB]
[数字图像处理与机器视觉：Visual.C++.与Matlab实现].张铮.扫描版.pdf [50.6 MB]
Computer and Machine Vision Theory Algorithms Practicalities.pdf [22.2 MB]
opencv2手册第五章.pdf [51.7 MB]
图像处理技术手册.pdf [145.1 MB]
OpenCV的计算机视觉技术实现.rar [13.6 MB]
数字图像处理与机器视觉――Visual C++与Matlab....iso [78.3 MB]
opencv手册.chm [2.6 MB]
OpenCV教程基础篇-于仕琪-北航.pdf [23.8 MB]
学习OpenCV 中文版.pdf [58.8 MB]
Mastering OpenCV with Practical Computer Vision Projects [eBook].pdf [6.3 MB]
计算机视觉（马颂德、张正友）.pdf [13.6 MB]
计算机视觉：算法与应用(Richard Szeliski-2010).pdf [22.1 MB]
📁 📁 12 深度学习CNN RNN等框架
📁 📁 第1课 机器学习中数学基础
第1课 机器学习中数学基础.avi [609.4 MB]
五月班第一次课件：机器学习中数学基础 (1).pdf [1.3 MB]
📁 📁 第9课 更多的网络类型
5月班第9次课课件_more_about_nn.pdf [4.2 MB]
第9课 更多的网络类型.avi [483.7 MB]
📁 📁 第6课 CNN推展案例
5月班第6次课 - CNN扩展 图像识别与定位 物体检测 NeuralStyle.pdf [48.3 MB]
第6课 CNN推展案例.avi [662.6 MB]
📁 📁 第3课 梯度下降法与反向传播
5月班第3课课件：梯度下降法与反向传播 (1).pdf [1.1 MB]
第3课 梯度下降法与反向传播.avi [438.7 MB]
📁 📁 第4课 CNN与常用框架
第4课 CNN与常用框架.avi [650.8 MB]
5月深度学习班第4课--CNN，典型网络结构与常用框架.pdf [7.0 MB]
📁 📁 第8课 RNN应用
第8课 RNN应用.avi [531.1 MB]
5月班第8课_rnn_appliacation.pdf [22.5 MB]
📁 📁 第2课 高效计算基础与图像线性分类器
numpy_operations.ipynb [207.4 KB]
image linear classification.zip [163.6 MB]
5月班第2课课件：高效计算基础与图像线性分类器.pdf [32.9 MB]
第2课 高效计算基础与图像线性分类器.avi [677.9 MB]
📁 📁 第5课 CNN训练注意事项与框架使用
第5课 CNN训练注意事项与框架使用.avi [743.3 MB]
5月班第5次课 - caffe TensorFlow使用与CNN训练注意事项.pdf [17.5 MB]
📁 📁 第10课 更多框架
第10课 更多框架.avi [429.4 MB]
5月班第10课_framework.pdf [22.0 MB]
📁 📁 第7课 RNN介绍
5月班第7课课件_rnn_intrduction.pdf [8.5 MB]
第7课 RNN介绍.avi [362.9 MB]
📁 📁 10 OpenCV计算机视觉实战(Python版)（课件+源码）
02、图像基本操作.mp4 [61.1 MB]
12、图像特征-sift.mp4 [81.3 MB]
16、背景建模.mp4 [52.1 MB]
11、图像特征-harris.mp4 [65.9 MB]
13、案例实战-全景图像拼接.mp4 [52.6 MB]
10、项目实战-文档扫描OCR识别.mp4 [76.2 MB]
15、项目实战-答题卡识别判卷.mp4 [61.5 MB]
17、光流估计.mp4 [53.5 MB]
14、项目实战-停车场车位识别.mp4 [290.1 MB]
19、项目实战-目标追踪.mp4 [117.7 MB]
07、图像金字塔与轮廓检测.mp4 [86.1 MB]
资料.zip [549.2 MB]
20、卷积原理与操作.mp4 [121.3 MB]
05、图像梯度处理.mp4 [35.9 MB]
21、项目实战-疲劳检测.mp4 [87.6 MB]
03、阈值与平滑处理.mp4 [32.7 MB]
18、Opencv的DNN模块.mp4 [28.0 MB]
01、课程简介.mp4 [43.0 MB]
08、直方图与傅里叶变换.mp4 [73.2 MB]
06、边缘检测.mp4 [29.1 MB]
09、项目实战-信用卡数字识别.mp4 [66.4 MB]
04、图像形态学处理.mp4 [27.0 MB]
📁 📁 07 OpenCV3.3深度神经网络(DNN)模块-应用视频教程 （课件+源码）
03-使用GoogleNet模型实现图像分类-02.ts [95.1 MB]
10-GOTURN模型实现视频对象跟踪.ts [142.5 MB]
08-FCN模型图像分割-02.ts [100.2 MB]
课程配套源代码.zip [20.4 MB]
课程配套PDF.zip [2.2 MB]
06-MobileNet模型实时对象检测.ts [110.1 MB]
02-使用GoogleNet模型实现图像分类-01.ts [117.6 MB]
01-DNN模块概述.ts [92.4 MB]
09-CNN模型预测性别与年龄.ts [146.8 MB]
04-使用SSD模型实现对象检测-01.ts [124.5 MB]
07-FCN模型实现图像分割-01.ts [102.9 MB]
05-使用SSD模型实现对象检测-02.ts [140.4 MB]
📁 📁 06 OpenCV视频分析与对象跟踪实战教程 （课件+源码）
02-视频读写-01.ts [61.4 MB]
18-扩展模块中的多对象跟踪.ts [93.3 MB]
09-光流的对象跟踪-02.ts [58.5 MB]
10-光流的对象跟踪-03.ts [137.6 MB]
03-视频读写-02.ts [111.9 MB]
11-光流的对象跟踪-04.ts [115.8 MB]
04-背景消除建模(BSM)-01.ts [84.9 MB]
05-背景消除建模(BSM)-02.ts [99.7 MB]
06-对象检测与跟踪(基于颜色)-01.ts [88.8 MB]
14-CAMShift对象跟踪-03.ts [123.4 MB]
12-CAMShift对象跟踪.ts [112.9 MB]
课程配套源代码.zip [55.0 MB]
07-对象检测与跟踪(基于颜色)-02.ts [79.0 MB]
16-视频中移动对象统计.ts [139.0 MB]
17-扩展模块中的跟踪方法介绍.ts [77.5 MB]
01-概述.ts [113.8 MB]
08-光流的对象跟踪-01.ts [134.7 MB]
15-CAMShift对象跟踪-04.ts [143.9 MB]
13-CAMShift对象跟踪-02.ts [62.8 MB]
📁 📁 14 附赠2：赠送不同环境下安装不同版本的opencv
5分钟配置好OpenCV3.2+VS2015开发环境.flv [20.1 MB]
win+OpenCV 4.0+Python3.6开发环境搭建.flv [113.7 MB]
win 系统 Visual Studio 2017安装及使用教程.flv [38.5 MB]
visual_studio_community_2017_version_15.3.exe [1.0 MB]
win+opencv3.3+VS2017环境配置指导.flv [194.0 MB]
📁 📁 03 OpenCV图像处理-小案例实战 （课件+源码）
10-案例四 对象计数-02.ts [118.1 MB]
11-案例五 透视校正-01.ts [125.5 MB]
13-案例五 透视校正-03.ts [139.2 MB]
04-案例一 切边-03.ts [125.4 MB]
06-案例二 直线检测-02.ts [104.5 MB]
02-案例一 切边-01.ts [83.5 MB]
课程配套PDF.zip [4.0 MB]
08-案例三 对象提取-02.ts [146.3 MB]
15-案例六 对象提取与测量.ts [130.0 MB]
课程配套源代码.zip [7.5 KB]
07-案例三 对象提取-01.ts [97.7 MB]
01-概述.ts [35.4 MB]
05-案例二 直线检测-01.ts [108.2 MB]
03-案例一 切边-02.ts [86.6 MB]
12-案例五 透视校正-02.ts [104.1 MB]
14-案例五 透视校正-04.ts [81.2 MB]
09-案例四 对象计数-01.ts [106.7 MB]
📁 📁 15 工具箱
📁 📁 4.0
opencv4.0.0.zip [86.8 MB]
opencv_contrib-4.0.0.zip [58.6 MB]
📁 📁 3.4
opencv-3.4.1-vc14_vc15.exe [171.9 MB]
vs2015.com_chs.iso [3.7 GB]
OpenCV下载地址.txt [199.0 B]
📁 📁 01 OpenCV图像处理视频课程（课件+源码）
30-凸包-Convex Hull.ts [140.7 MB]
02-加载、修改、保存图像.ts [97.1 MB]
34-基于距离变换与分水岭的图像分割-01.ts [169.6 MB]
13-形态学操作应用-提取水平与垂直线.ts [139.9 MB]
09-模糊图像一.ts [119.8 MB]
05-图像操作.ts [107.2 MB]
35-基于距离变换与分水岭的图像分割-02.ts [115.8 MB]
31-轮廓周围绘制矩形框和圆形框.ts [146.4 MB]
11-膨胀与腐蚀.ts [118.5 MB]
14-图像金字塔-上采样与降采样.ts [121.7 MB]
22-霍夫圆变换.ts [101.9 MB]
26-直方图比较.ts [159.7 MB]
17-处理边缘.ts [101.2 MB]
08-绘制形状与文字.ts [170.7 MB]
23-像素重映射(cv__remap).ts [126.5 MB]
29-轮廓发现.ts [134.1 MB]
07-调整图像亮度与对比度.ts [114.0 MB]
18-Sobel算子.ts [177.0 MB]
21-霍夫变换-直线.ts [113.0 MB]
课程配套PPT.zip [32.7 MB]
33-点多边形测试.ts [124.2 MB]
32-图像矩(Image Moments).ts [158.4 MB]
06-图像混合.ts [83.7 MB]
03-矩阵的掩膜操作.ts [151.6 MB]
24-直方图均衡化.ts [83.0 MB]
25-直方图计算.ts [118.2 MB]
01-概述 - OpenCV介绍与环境搭建.ts [120.8 MB]
10-图像模糊二.ts [143.4 MB]
16-自定义线性滤波.ts [152.8 MB]
12-形态学操作.ts [106.5 MB]
27-直方图反向投影(Back Projection).ts [177.2 MB]
课程配套源代码.zip [5.8 MB]
04-Mat对象.ts [144.2 MB]
20-Canny边缘检测.ts [140.3 MB]
15-基本阈值操作.ts [137.9 MB]
19-Laplance算子.ts [64.1 MB]
28-模板匹配(Template Match).ts [157.3 MB]
00 如果没有声音或者卡顿，下载到电脑看即可.txt
📁 阶段3-数据处理与统计分析
📁 📁 day09
16_RFM计算流程梳理&问题说明.mp4 [28.0 MB]
06_Seaborn绘图_双变量可视化.mp4 [33.8 MB]
11_RFM案例数据介绍.mp4 [8.5 MB]
02_pandas双变量可视化_散点图蜂巢图和堆叠柱状图.mp4 [29.0 MB]
08_Seaborn绘图_成对关系图和多变量可视化.mp4 [25.9 MB]
15_RFM案例计算完成.mp4 [28.9 MB]
14_RFM案例_三个维度聚合值的计算.mp4 [12.0 MB]
01_内容回顾.mp4 [25.7 MB]
09_RFM模型业务介绍.mp4 [17.4 MB]
04_Seaborn绘图KDE和直方图.mp4 [28.8 MB]
12_RFM案例_数据加载和数据清洗.mp4 [27.4 MB]
18_RFM计算完成_结果可视化.mp4 [28.3 MB]
05_Seaborn绘图计数柱状图.mp4 [11.1 MB]
13-RFM案例_把数据拼接到一起.mp4 [20.5 MB]
10_RFM适合落地场景.mp4 [7.2 MB]
03_pandas双变量可视化小结.mp4 [16.6 MB]
07_Seaborn绘图, 双变量可视化.mp4 [27.7 MB]
📁 📁 day02
15_今日小结(2).mp4 [18.5 MB]
13_SQL中的DDL_数据表的操作.mp4 [22.0 MB]
02_linux常用快捷键.mp4 [15.4 MB]
05_网络操作文件下载.mp4 [52.4 MB]
03_软件安装开启关闭系统服务和软连接.mp4 [29.0 MB]
04_ip地址和域名解析.mp4 [21.1 MB]
09_数据库简介.mp4 [8.6 MB]
10_SQL语言简介.mp4 [25.5 MB]
06_端口占用查看和进程查询.mp4 [45.5 MB]
08_压缩解压缩.mp4 [35.3 MB]
01_内容回顾.mp4 [20.9 MB]
12_SQL中的DDL_数据库操作.mp4 [10.1 MB]
14_SQL中的DML操作.mp4 [22.7 MB]
11_SQL数据类型介绍.mp4 [8.8 MB]
07_环境变量配置.mp4 [40.1 MB]
📁 📁 day01
02_Linux系统简介.mp4 [11.5 MB]
09_mkdir创建文件夹.mp4 [8.7 MB]
14_vi和vim编辑器介绍.mp4 [21.5 MB]
17_linux权限介绍.mp4 [31.9 MB]
01_操作系统简介.mp4 [6.1 MB]
03_虚拟机介绍.mp4 [14.7 MB]
04_finalshell介绍&vmware网络配置.mp4 [19.9 MB]
08_绝对路径和相对路径.mp4 [13.6 MB]
16_linux普通用户和超级管理员介绍.mp4 [25.1 MB]
05_快照介绍.mp4 [10.3 MB]
12_内容过滤grep和管道符.mp4 [23.2 MB]
06_linux目录结构和命令介绍.mp4 [14.7 MB]
11_which和find查找.mp4 [16.4 MB]
13_文件内容修改_echo重定向符tail命令.mp4 [23.8 MB]
10_文件文件夹的创建查看移动复制和删除.mp4 [36.2 MB]
15_linux命令的帮助和命令手册.mp4 [12.8 MB]
07_切换工作目录_cd和pwd.mp4 [24.8 MB]
18_linux权限介绍chmod和chown.mp4 [33.0 MB]
19_内容回顾.mp4 [13.2 MB]
📁 📁 day07
12_线上线下会员增量分析.mp4 [10.5 MB]
07_分组过滤.mp4 [13.6 MB]
05_分组转换小结.mp4 [9.6 MB]
03_分组聚合.mp4 [17.2 MB]
10_会员分析&数据透视表_会员增量等级分析.mp4 [30.9 MB]
13_地区店均会员分析.mp4 [35.6 MB]
15_内容回顾.mp4 [20.9 MB]
04_分组转换.mp4 [32.9 MB]
02_向量化函数和Lambda表达式.mp4 [26.0 MB]
08_DataFrameGroupby对象.mp4 [23.8 MB]
06_分组转换练习.mp4 [21.4 MB]
09_会员分析&数据透视表_会员增量和存量分析.mp4 [36.3 MB]
01_内容回顾.mp4 [44.9 MB]
14_地区会销比计算.mp4 [43.0 MB]
11_存量等级分布分析.mp4 [22.3 MB]
📁 📁 day04
07_Jupyterlab环境搭建.mp4 [18.8 MB]
17_今日小结.mp4 [12.7 MB]
10_numpy的ndarray的创建2.mp4 [14.5 MB]
01_SQL内容回顾.mp4 [41.3 MB]
03_窗口函数简单应用.mp4 [26.7 MB]
14_两个ndarray之间的运算.mp4 [16.0 MB]
15_pandas数据结构_Series和DataFrame创建.mp4 [17.1 MB]
05_LinuxSQL小结.mp4 [8.2 MB]
08_Jupyternotebook的使用.mp4 [15.7 MB]
04_窗口函数_排序函数.mp4 [38.3 MB]
02_窗口函数简介.mp4 [17.9 MB]
06_Python数据处理分析简介.mp4 [11.7 MB]
11_numpy的ndarray的创建3.mp4 [15.8 MB]
13_numpy的内置函数完成.mp4 [18.4 MB]
09_numpy的ndarray的属性和创建.mp4 [21.1 MB]
12_numpy的内置函数_基本运算.mp4 [19.1 MB]
16_Series常用方法.mp4 [34.5 MB]
📁 📁 day03
01_内容回顾.mp4 [13.6 MB]
10_SQL_DQL子查询和自连接小结.mp4 [11.3 MB]
06_SQL_DQL多表查询关联查询介绍.mp4 [19.6 MB]
13_SQL报表练习_CASEWHEN.mp4 [37.3 MB]
14_今日重点.mp4 [24.2 MB]
03_SQL_DQL条件查询范围查询.mp4 [14.4 MB]
12_SQL报表练习_分组聚合.mp4 [52.8 MB]
05_SQL_DQL多表查询.mp4 [23.4 MB]
07_SQL_DQL多表查询关联查询案例说明.mp4 [18.5 MB]
09_SQL_DQL多表查询练习_子查询和自连接.mp4 [29.7 MB]
08_SQL_DQL多表查询练习.mp4 [26.0 MB]
04_SQL_DQL单表查询完成.mp4 [29.3 MB]
11_SQL报表练习.mp4 [55.1 MB]
02_SQL_约束介绍.mp4 [24.9 MB]
📁 📁 day10
10_优衣库销售数据分析_售价和成本之间关系.mp4 [8.4 MB]
02_内容回顾_RFM.mp4 [16.7 MB]
11_内容回顾_Pandas.mp4 [24.8 MB]
08_优衣库销售数据分析_整体思路和类别销售情况分析.mp4 [20.0 MB]
09_消费偏好_线上线下周间周末.mp4 [31.6 MB]
03_app_store业务介绍&数据加载和清洗.mp4 [27.8 MB]
13_内容回顾 SQL2.mp4 [17.5 MB]
12_内容回顾_SQL.mp4 [25.9 MB]
06_业务数据可视化.mp4 [24.0 MB]
05_业务数据客户化和业务解读说明.mp4 [19.3 MB]
14_Linux内容回顾.mp4 [21.4 MB]
01_内容回顾可视化.mp4 [19.4 MB]
04_单变量分析.mp4 [31.8 MB]
07_业务问题解答.mp4 [20.5 MB]
📁 📁 day05
17_查看数据情况&排序方法小结.mp4 [15.0 MB]
18_今日内容小结.mp4 [17.1 MB]
09_DataFrame行列索引的修改小结.mp4 [17.1 MB]
12_DataFrame加载部分数据.mp4 [40.1 MB]
02_虚拟环境问题说明.mp4 [13.5 MB]
04_布尔索引小结.mp4 [10.6 MB]
05_Series的运算.mp4 [13.3 MB]
16_Pandas数据分析练习_加载数据之后查看数据情况&常用排序方法.mp4 [30.9 MB]
07_DataFrame索引的调整.mp4 [13.9 MB]
03_Series的布尔索引.mp4 [22.9 MB]
11_DataFrame数据的保存跟加载.mp4 [18.1 MB]
06_DataFrame的常用操作.mp4 [31.1 MB]
10_DataFrame插入删除追加一列数据.mp4 [27.1 MB]
13_DataFrame分组聚合计算.mp4 [42.3 MB]
15_DataFrame简单可视化说明.mp4 [11.2 MB]
14_DataFrame分组聚合小结.mp4 [5.7 MB]
01_内容回顾.mp4 [23.4 MB]
08_DataFrame行列索引的修改.mp4 [18.7 MB]
📁 📁 day08
13_数据可视化简介.mp4 [19.3 MB]
06_日期时间类型_获取日期中的不同部分.mp4 [17.4 MB]
17_Matplotlib的双变量和多变量可视化.mp4 [17.6 MB]
16_Matplotlib单变量可视化_直方图.mp4 [18.9 MB]
08_日期时间索引.mp4 [20.6 MB]
19_pandas绘图_单变量可视化_直方图和饼图.mp4 [15.7 MB]
15_Matplotlib案例_anscome数据集可视化.mp4 [17.5 MB]
20_今日内内容小结.mp4 [15.5 MB]
05_Pandas的日期时间类型简介.mp4 [14.5 MB]
03_会员消费复购率计算_1.mp4 [22.5 MB]
14_Matplotlib的API介绍.mp4 [17.9 MB]
09_生成日期时间序列.mp4 [24.6 MB]
07_日期时间类型_timedelta类型.mp4 [14.7 MB]
10_日期时间数据类型小结.mp4 [17.6 MB]
18_pandas的绘图_单变量可视化_柱状图和折线图面积图.mp4 [36.7 MB]
01_内容回顾.mp4 [18.7 MB]
12_日期时间类型练习.mp4 [38.3 MB]
02_会员消费连带率计算.mp4 [22.7 MB]
04_会员消费复购率计算完成.mp4 [27.7 MB]
11_日期时间类型练习说明.mp4 [10.9 MB]
📁 📁 day06
10_缺失值处理_缺失值处理和非时序数据缺失值填充.mp4 [24.8 MB]
07_DataFrame数据组合_join连接.mp4 [26.6 MB]
03_租房数据练习小结.mp4 [21.4 MB]
04_DataFrame数据组合_concat连接.mp4 [20.9 MB]
12_series的apply方法.mp4 [14.1 MB]
15_内容回顾.mp4 [19.7 MB]
13_dataframe的apply方法.mp4 [30.5 MB]
11_缺失值处理_时序数据填充&小结.mp4 [30.1 MB]
14_apply的应用练习.mp4 [8.4 MB]
08_缺失值处理_缺失值简介和判断.mp4 [16.2 MB]
06_DataFrame数据组合_merge连接小结.mp4 [28.1 MB]
09_缺失值处理_加载包含确实的数据和缺失值统计.mp4 [25.9 MB]
02_租房数据练习.mp4 [46.9 MB]
05_DataFrame数据组合_merge连接.mp4 [28.3 MB]
01_内容回顾.mp4 [18.0 MB]
📁 赠送：AI关系抽取项目
📁 📁 day04
08-extract_sub函数代码分析_.mp4 [120.6 MB]
12-train_eopch和modle2dev函数讲解_.mp4 [124.3 MB]
07-load_model函数代码实现_.mp4 [73.0 MB]
10-extract_obj_and_relation函数的分析_.mp4 [16.1 MB]
14-train函数代码的实现_.mp4 [60.7 MB]
09-extract_sub代码实现_.mp4 [10.0 MB]
15-test函数代码实现_.mp4 [98.8 MB]
11-extract_obj_and_rel函数的实现_.mp4 [12.8 MB]
01-内容回顾_.mp4 [26.6 MB]
02-Casrel模型init函数实现_.mp4 [27.6 MB]
05-Casrel模型forward函数实现_.mp4 [19.6 MB]
13--train函数讲解_.mp4 [19.7 MB]
03-Casrel模型get_encode_result和get_subs代码实现_.mp4 [83.9 MB]
06-Casrel模型代码搭建实现_.mp4 [37.0 MB]
04-Casrel模型客实体识别代码_.mp4 [74.5 MB]
📁 📁 day02
15--模型数据形状结果分析_.mp4 [27.5 MB]
17--预测代码分析_.mp4 [13.8 MB]
09--Dataset类和Dataloader类实现_.mp4 [22.7 MB]
11-init方法代码实现_.mp4 [40.8 MB]
14--foward函数代码实现_.mp4 [27.2 MB]
05-get_txt_data函数代码实现_.mp4 [28.4 MB]
18--模型预测代码的实现_.mp4 [46.0 MB]
16--模型训练_.mp4 [50.4 MB]
12--初始化参数方法和attention方法实现_.mp4 [18.6 MB]
08--自定义函数collate_fn函数的实现_.mp4 [43.2 MB]
04-数据预处理函数get_txt_data讲解_.mp4 [23.4 MB]
01-昨日视频回顾_.mp4 [44.2 MB]
06--get_word_id函数实现_.mp4 [31.4 MB]
03-数据预处理pos函数和pos_padding函数的实现_.mp4 [19.7 MB]
10--BiLSTM+Attention模型类init方法讲解_.mp4 [17.8 MB]
13--forward函数代码分析_.mp4 [38.7 MB]
02-数据预处理Pos函数讲解_.mp4 [6.2 MB]
07--DataSet类的实现_.mp4 [15.2 MB]
19--今日内容总结_.mp4 [19.4 MB]
📁 📁 day01
05-关系抽取任务的指标和问题_.mp4 [27.5 MB]
08-规则实现关系抽取代码分析_.mp4 [24.9 MB]
02-关系抽取项目背景介绍_.mp4 [24.8 MB]
11-BiLSTM+Attention模型架构分析_.mp4 [7.2 MB]
03-关系抽取其业务他应用场景_.mp4 [13.2 MB]
14--数据预处理函数sent_padding实现_.mp4 [53.7 MB]
01-关系抽取项目简介_.mp4 [30.4 MB]
07--规则进行关系抽取任务介绍_.mp4 [25.5 MB]
04-关系抽取任务的特点_.mp4 [15.9 MB]
09-规则实现关系抽取总结mp4_.mp4 [9.2 MB]
13-BILSTM+Attention数据预处理函数配置_.mp4 [16.9 MB]
06--关系抽取第一小结_.mp4 [14.9 MB]
12--BiLSTM+Attention模型配置文件_.mp4 [39.1 MB]
10-Pipeline方式原理介绍_.mp4 [30.8 MB]
📁 📁 day03
03-Joint方法优缺点_.mp4 [3.1 MB]
07-Config类代码的实现_.mp4 [39.0 MB]
11-create_label代码实现_.mp4 [81.7 MB]
12-collate_fn自定义函数代码分析_.mp4 [26.8 MB]
14-Dataset类代码实现_.mp4 [21.4 MB]
06-Casrel数据集介绍_.mp4 [22.2 MB]
05-Casrel模型架构_.mp4 [8.5 MB]
01-知识回顾_.mp4 [19.9 MB]
15-Dataloader类代码分析和实现_.mp4 [27.5 MB]
13--collate_fn自定义函数实现_.mp4 [34.8 MB]
02-Joint方法原理介绍_.mp4 [23.5 MB]
09-数据预处理函数creat_label思想_.mp4 [188.2 MB]
08-第一个数据处理函数find_head_index实现_.mp4 [15.9 MB]
10-create_label代码思想_.mp4 [45.0 MB]
04-Casrel模型架构讲解_.mp4 [32.8 MB]
📁 📁 day05
08-关系查询_.mp4 [65.1 MB]
03-API接口的测试_.mp4 [36.1 MB]
04-Neo4j图数据库安装介绍_.mp4 [25.2 MB]
10-py2neo操作neo4j结尾_.mp4 [19.9 MB]
09-节点和关系的删除_.mp4 [127.4 MB]
06-Neo4j图数据库创建关系_.mp4 [12.5 MB]
07-Neo4j图数据库查询节点_.mp4 [41.8 MB]
13-将所有预测的SPO三元组数据导入Neo4j_.mp4 [38.0 MB]
05-Neo4j图数据库创建节点_.mp4 [45.3 MB]
01-知识回顾_.mp4 [27.8 MB]
12-Neo4j创建节点函数_.mp4 [27.4 MB]
02-API接口的制作_.mp4 [32.3 MB]
11-Neo4j图数据库准备数据_.mp4 [82.5 MB]
📁 阶段10-投满分项目V4
📁 📁 day06
03-项目串讲_.mp4 [16.5 MB]
02-数据集构建方法_.mp4 [7.2 MB]
01-面试问题和工作文问题_.mp4 [60.8 MB]
📁 📁 day03
05-模型预测_.mp4 [73.1 MB]
02-模型训练与评估思想_.mp4 [35.4 MB]
04-实现2_.mp4 [75.8 MB]
07-模型量化_.mp4 [32.7 MB]
01-模型构建_.mp4 [199.7 KB]
06-模型部署_.mp4 [28.9 MB]
03-模型训练与评估实现_.mp4 [79.0 MB]
📁 📁 day02
03-模型部署_.mp4 [59.5 MB]
07-数据迭代_.mp4 [103.5 MB]
06-bert数据获取_.mp4 [144.3 MB]
04-bert数据信息_.mp4 [44.3 MB]
08-时间差计算_.mp4 [10.1 MB]
05-bert代码结构构建_.mp4 [10.5 MB]
02-模型训练_.mp4 [11.2 MB]
01-fasttext优化-分词_.mp4 [16.8 MB]
📁 📁 day01
02-数据集获取_.mp4 [32.9 MB]
05-数据获取_.mp4 [23.4 MB]
07-模型构建与训练_.mp4 [16.0 MB]
01-项目背景和数据集介绍_.mp4 [72.7 MB]
08-fasttext数据处理_.mp4 [32.2 MB]
04-分词_.mp4 [29.5 MB]
11-优化1-自动化参数搜索_.mp4 [24.7 MB]
10-fasttext模型训练_.mp4 [15.6 MB]
06-特征工程_.mp4 [23.6 MB]
03-数据分布分析_.mp4 [20.4 MB]
09-fasttext数据集构建_.mp4 [36.4 MB]
📁 📁 day05
04-损失计算_.mp4 [47.5 MB]
12-自定义剪枝_.mp4 [22.5 MB]
06-主函数_.mp4.mp4 [33.6 MB]
02-textCNN介绍_.mp4 [55.0 MB]
08-特定层剪枝_.mp4 [53.3 MB]
07-剪枝思想_.mp4 [11.8 MB]
09-结构化剪枝_.mp4 [11.9 MB]
05-模型训练_.mp4 [62.0 MB]
11-全局剪枝_.mp4 [28.7 MB]
10-多层剪枝_.mp4 [33.5 MB]
03-数据对齐_.mp4 [30.2 MB]
06-训练流程_.mp4 [33.3 MB]
01-内容回顾_.mp4 [15.5 MB]
📁 📁 day04
08-textCNN实践_.mp4 [86.9 MB]
05-数据获取_.mp4 [91.3 MB]
04-词表构建_.mp4 [66.9 MB]
02-模型蒸馏思想_.mp4 [51.6 MB]
07-数据迭代实现_.mp4 [54.1 MB]
06-数据获取实现_.mp4 [14.3 MB]
01-昨日回顾_.mp4 [37.7 MB]
03-模型蒸馏项目架构_.mp4 [13.4 MB]
📁 阶段9-算法初识
12_查找问题_两个数组的交集_.mp4 [13.7 MB]
35_下午内容小结_.mp4 [15.0 MB]
17_链表概念介绍_.mp4 [18.1 MB]
10_字符串问题_借助字典解决问题_.mp4 [17.6 MB]
02_排序算法回顾_.mp4 [27.2 MB]
34_贪心问题_.mp4 [10.5 MB]
06_数组问题_对撞指针_.mp4 [17.6 MB]
19_链表相关问题_反转链表_.mp4 [9.5 MB]
33_动态规划问题_.mp4 [17.1 MB]
07_数组问题_滑动窗口_.mp4 [8.9 MB]
24_队列相关问题1_.mp4 [12.2 MB]
31_回溯问题_数字全排列_.mp4 [14.3 MB]
27_递归_二叉树的遍历问题_.mp4 [21.7 MB]
25_队列问题_滑动窗口最大值_.mp4 [11.7 MB]
01_算法面试简介_.mp4 [17.4 MB]
14_查找问题_两数之和_.mp4 [4.1 MB]
11_字符串问题_字符顺序列表说明&ord_.mp4 [11.2 MB]
30_递归问题_电话号码的字母组合_.mp4 [8.3 MB]
04_数组问题_移除元素_.mp4 [8.6 MB]
32_动态规划简介_.mp4 [13.2 MB]
23_队列概念介绍_.mp4 [5.8 MB]
16_今日回顾_.mp4 [11.6 MB]
03_数组问题_移动0_.mp4 [16.8 MB]
28_递归问题_树的最大深度和翻转二叉树_.mp4 [6.9 MB]
08_数组问题_滑动窗口小结_.mp4 [7.1 MB]
18_给链表添加常用方法_.mp4 [8.8 MB]
09_字符串问题_双指针和滑动窗口_.mp4 [13.1 MB]
21_栈介绍&有效的括号_.mp4 [18.0 MB]
29_递归问题_路径总和_.mp4 [7.7 MB]
22_栈问题_最小栈_.mp4 [4.2 MB]
20_链表相关问题_2_.mp4 [12.1 MB]
15_查找问题_存在重复元素_.mp4 [25.2 MB]
13_查找问题_同构字符串_.mp4 [23.5 MB]
05_数组问题_删除有序数组中的重复项_.mp4 [10.8 MB]
26_链表栈队列小结_.mp4 [9.8 MB]
📁 阶段12-CHAT_GPT与大模型
📁 📁 day04
02-ChatPrompt应用_.mp4 [44.7 MB]
13-向量数据库_.mp4 [62.0 MB]
03-Embedding模型_.mp4 [41.9 MB]
12-文档分割_.mp4 [54.5 MB]
16-BERT-PET方法文本分类介绍_(已加密).mp4 [66.7 MB]
15-第三章内容总结_.mp4 [45.7 MB]
10-message_dict的使用_.mp4 [30.1 MB]
08-Agent代理_.mp4 [109.5 MB]
04-Prompt提示-zero-shot模版构建_.mp4 [44.3 MB]
14-文档检索器_.mp4 [31.1 MB]
06-Chain工具的应用_.mp4 [15.7 MB]
07-Sequential_Chain的应用_.mp4 [50.5 MB]
01-ChatModels的应用_.mp4 [73.5 MB]
09-Memory_.mp4 [55.7 MB]
11-文档加载器的使用_.mp4 [45.1 MB]
05-Prompt提示-few-shot模版构建_.mp4 [49.4 MB]
📁 📁 day02
04-GPT3模型介绍_.mp4 [85.3 MB]
10-BLOOM大模型_.mp4 [36.8 MB]
01-昨日内容复习_.mp4 [70.6 MB]
03-GPT2模型介绍_.mp4 [83.1 MB]
13-百度千帆大模型应用平台注册使用_.mp4 [48.5 MB]
12-Prompt-Tuning介绍_.mp4 [89.0 MB]
06-ChatGPT原理学习_.mp4 [70.9 MB]
11-Fine-Tuning思想回顾_.mp4 [31.7 MB]
02-ChatGPT基本介绍_.mp4 [17.7 MB]
08-ChatGLM-6B讲解_.mp4 [49.5 MB]
09-LLaMA模型讲解_.mp4 [74.3 MB]
05-理解强化学习思想_.mp4 [54.7 MB]
07-GLM架构思想_.mp4 [67.8 MB]
📁 📁 day01
13-BERT模型特点_.mp4 [21.3 MB]
16-大模型Decoder-only的原因_.mp4 [19.4 MB]
02-大模型背景基础知识_.mp4 [32.9 MB]
06-基于transformer的预训练语言模型_.mp4 [25.6 MB]
03-语言模型介绍_.mp4 [25.1 MB]
01-大模型整体课程介绍_.mp4 [42.5 MB]
10-PPL困惑度计算_.mp4 [59.0 MB]
11-LLM架构类型_.mp4 [19.9 MB]
08-BlEU指标计算_.mp4 [119.7 MB]
04-N-Gram语言模型介绍_.mp4 [52.2 MB]
15-T5模型讲解_.mp4 [43.3 MB]
05-神经网络语言模型_.mp4 [25.6 MB]
07-大语言模型的介绍_.mp4 [17.6 MB]
14-GPT模型讲解_.mp4 [75.7 MB]
12-BERT模型的架构介绍_.mp4 [40.9 MB]
📁 📁 day03
16-LLMs组件应用_.mp4 [76.3 MB]
14-Lora的思想_.mp4 [33.0 MB]
08-P-tuning的思想_.mp4 [32.5 MB]
05-Prompt-Oriented-Fine-Tuning_.mp4 [41.5 MB]
12-Prefix-Tuning_.mp4 [79.4 MB]
10-Instruction-Tuning思想_.mp4 [103.3 MB]
17-不同模型选择LangChain_.mp4 [12.8 MB]
07-Prompt_tuning论文思想引入_.mp4 [37.1 MB]
13-Adapter_Tuning思想_.mp4 [17.9 MB]
01-昨日内容回顾_.mp4 [156.2 MB]
02-引入Prompt-Tuning思想_.mp4 [30.0 MB]
04-PET模型思想介绍_.mp4 [43.8 MB]
09-PPT-模型思想_.mp4 [31.2 MB]
11-COT思想_.mp4 [43.1 MB]
03-GPT3提出Prompt前身思想_.mp4 [51.5 MB]
06-Hard-Prompt-VS-Soft-Prompt_.mp4 [58.1 MB]
📁 阶段2-Python编程进阶
📁 📁 day04-闭包装饰器
09-【重要】1使用闭包解方式-需求_.mp4 [4.5 MB]
27-【了解】三次握手_.mp4 [33.1 MB]
28-【了解】四次挥手_.mp4 [33.2 MB]
30-【了解】网络协议-基本工作过程_.mp4 [39.4 MB]
20-【了解】多个装饰器修饰同一个原函数-2代码实现_.mp4 [27.1 MB]
05-【了解】如何缓存函数中的变量_.mp4 [15.2 MB]
07-【重要】闭包执行顺序_.mp4 [16.2 MB]
17-【了解】中午课程复习_.mp4 [5.8 MB]
09-【重要】2使用闭包解方式-实现_.mp4 [24.9 MB]
06-【重要】闭包的语法_.mp4 [60.6 MB]
13-【了解】无参无返回的原函数-装饰_.mp4 [19.9 MB]
02-【了解】回调函数_.mp4 [52.5 MB]
03-【重要】混合类型-深前拷贝_.mp4 [78.9 MB]
19-【了解】不定长参数的原函数-装饰_.mp4 [28.8 MB]
09-【重要】3闭包中函数嵌套调用-拓展_.mp4 [2.6 MB]
01-【了解】学生管理系统-复习_.mp4 [67.5 MB]
15-【了解】无参数有返回值的原函数-装饰_.mp4 [11.9 MB]
18-【了解】中午课程回顾_.mp4 [44.5 MB]
20-【了解】多个装饰器修饰同一个原函数-1思路分析_.mp4 [52.0 MB]
24-【了解】网络概念_ip地址_.mp4 [78.4 MB]
21-【重要】课堂答疑-返回内部函数的入口地址_.mp4 [19.6 MB]
29-【了解】小结_.mp4 [12.2 MB]
22-【了解】带参数的装饰器-1错误语法_.mp4 [24.9 MB]
11-【重要】装饰器语法-代码实现_.mp4 [13.8 MB]
10-【重要】装饰器语法-思路分析_.mp4 [28.0 MB]
12-【重要】语法糖-基本语法_.mp4 [21.3 MB]
26-【了解】协议-概念_.mp4 [19.5 MB]
08-【重要】闭包基本语法实现_.mp4 [28.2 MB]
16-【了解】有参数有返回值的原函数-装饰_.mp4 [8.8 MB]
14-【了解】有参无返回值的原函数-装饰_.mp4 [8.8 MB]
23-【了解】带参数的装饰器-3本质复现_.mp4 [19.3 MB]
04-【了解】直接调用和间接调用_.mp4 [27.0 MB]
23-【了解】带参数的装饰器-2正确语法_.mp4 [20.6 MB]
25-【重要】端口号-标识是哪一个应用程序_.mp4 [15.4 MB]
📁 📁 day02-面向对象高级
06-【了解】继承语法_.mp4 [23.2 MB]
18-【重要】1手工调用父类init_.mp4 [12.7 MB]
14-【了解】2课堂答疑-显示调用父类的被子类重新的方法-需要手工调用init_.mp4 [25.4 MB]
18-【重要】3小结_.mp4 [10.4 MB]
14-【了解】1课堂答疑-显示调用父类的被子类重新的方法-需要手工调用init_.mp4 [24.1 MB]
20-【了解】中午课程回顾_.mp4 [62.5 MB]
22-【重要】多态成立的三个条件_.mp4 [20.1 MB]
11-【了解】继承小结_.mp4 [1.8 MB]
08-【重要】多继承-继承顺序-思路分析_.mp4 [16.2 MB]
26-【重要】3多态和抽象类小结_.mp4 [9.0 MB]
17-【了解】小结_.mp4 [12.8 MB]
10-【重要】课堂答疑如何刨祖坟_.mp4 [21.4 MB]
13【了解】子类显示的调用父类属性和方法_.mp4 [29.7 MB]
07-【重要】单继承_.mp4 [25.0 MB]
12【了解】子类重写父类属性和方法_.mp4 [14.4 MB]
25-【重要】多态的意义_.mp4 [57.5 MB]
26-【重要】2接口抽象类-代码实现_.mp4 [18.9 MB]
21-【重要】2多态概念-代码实现_.mp4 [25.4 MB]
24-【重要】python中只要长得像就可以多态_.mp4 [25.7 MB]
23-【重要】1案例搭建多态场景-思路分析_.mp4 [46.1 MB]
03-【了解】每日反馈_.mp4 [41.4 MB]
18-【重要】2手工调用父类init_.mp4 [2.5 MB]
28-【了解】类方法-类方法操作类属性_.mp4 [20.8 MB]
31-【了解】有关_name__课堂答疑_.mp4 [32.4 MB]
09-【重要】多继承-继承顺序-mro代码实现_.mp4 [44.7 MB]
26-【重要】1接口抽象类-概念_.mp4 [20.3 MB]
04-【了解】作业复习_.mp4 [44.7 MB]
21-【重要】1多态概念-思路分析_.mp4 [18.8 MB]
29-【了解】静态方法_.mp4 [18.2 MB]
23-【重要】2案例搭建多态场景-代码实现_.mp4 [29.0 MB]
01-【了解】上一课程复习_.mp4 [33.8 MB]
16-【难点重要】super常见问题_.mp4 [8.7 MB]
05-【了解】定义类的三种方法_.mp4 [16.8 MB]
15-【了解】super常见问题_.mp4 [12.3 MB]
19-【重要】私有属性和方法_.mp4 [41.7 MB]
02-【了解】代码复习_.mp4 [23.2 MB]
27-【了解】类的属性_.mp4 [39.2 MB]
30-【了解】作业和小结_.mp4 [34.8 MB]
📁 📁 day05-网络编程下和多任务编程上
23-【重要】课堂答疑-函数入口地址不要写成函数调用_.mp4 [4.5 MB]
06-【重要】客户端和服务器端-基本原理_.mp4 [70.3 MB]
25-【了解】进程编号_.mp4 [36.4 MB]
24-【重要】多进程带参数边代码边执行_.mp4 [21.2 MB]
07-【重要】客户端和服务器端-代码分析_.mp4 [35.0 MB]
22-【重要】多进程边代码边音乐_.mp4 [44.3 MB]
13-【重要】一个服务器支持多个客户端_.mp4 [20.2 MB]
20-【了解】进程概念_.mp4 [41.7 MB]
21-【了解】单进程边代码边音乐_.mp4 [22.9 MB]
16-【重要】数据类型转换_.mp4 [18.0 MB]
29-2【重要】2主进程创建守候进程_.mp4 [18.3 MB]
01-【了解】复习装饰器_.mp4 [71.6 MB]
26-【重要】1进程间不共享数据-思路分析_.mp4 [15.6 MB]
27-【重要】主进程的资源是指所有mian条件以外的代码_.mp4 [4.4 MB]
12-【拓展】长连接和端连接_.mp4 [44.7 MB]
17-【了解】ppt讲义小结_.mp4 [35.0 MB]
09-【重要】客户端程序-编码实现_.mp4 [28.3 MB]
15-【重要】api函数机理小结_.mp4 [48.3 MB]
04-【了解】tcpip协议复习_.mp4 [7.2 MB]
29-3【了解】主进程对子进程管理小结_.mp4 [17.6 MB]
26-【重要】2进程间不共享数据-实验证明_.mp4 [66.9 MB]
14-【重要】socketapi的深入分析_.mp4 [44.5 MB]
28-【重要】创建子进程的代码必须写在main条件下_.mp4 [19.1 MB]
19-【了解】多任务概念_.mp4 [17.7 MB]
18-【了解】中午课程回顾_.mp4 [27.6 MB]
08-【重要】服务器程序-编码实现_.mp4 [37.5 MB]
03-【了解】tcpip协议复习_.mp4 [26.8 MB]
29-【重要】1主进程等待子进程接受以后再结束_.mp4 [19.5 MB]
02-【了解】作业题串讲_.mp4 [37.2 MB]
11-【重要】课堂问题-端口复用属性设置_.mp4 [13.8 MB]
05-【了解】创建socket对象_.mp4 [59.9 MB]
10-【重要】课堂问题12_.mp4 [10.9 MB]
📁 📁 day07-正则表达式和时间复杂度
28-【了解】4顺序表数据删除和添加小结_.mp4 [13.1 MB]
20-【了解】算法的特性_.mp4 [24.2 MB]
05-【重要】正则r-sub用法_.mp4 [19.3 MB]
08-【了解】匹配单个字符小结_.mp4 [12.0 MB]
03-【重要】正则表达是概念-match思路分析_.mp4 [34.6 MB]
25-【重要】常见时间复杂度_.mp4 [32.3 MB]
26-【了解】空间复杂度_.mp4 [32.9 MB]
10-【了解】匹配多个字符小结_.mp4 [18.3 MB]
14-【了解】2分组相关_.mp4 [22.6 MB]
22-【了解】2算法的复杂度-比较2个算法好坏_.mp4 [5.3 MB]
28-【了解】3一体式结构和分离式结构扩展策略_.mp4 [22.9 MB]
21-【了解】算法时间效率-2个因素_.mp4 [26.7 MB]
07-【重要】匹配单个字符2_.mp4 [20.5 MB]
01-【了解】多线程复习_.mp4 [78.4 MB]
18-【了解】数据结构概念_.mp4 [51.9 MB]
12-【重要】匹配开头和结束-1_.mp4 [24.5 MB]
17-【重要】正则小练习_.mp4 [10.3 MB]
15-【了解】3分组引用起个别名_.mp4 [38.8 MB]
13-【重要】匹配开头和结束-2_.mp4 [38.2 MB]
24-【了解】最优-坏时间复杂度_.mp4 [13.7 MB]
27-【了解】复习时间结构+算法=程序_.mp4 [17.7 MB]
22-【了解】1算法的复杂度-大O计数法_.mp4 [11.4 MB]
19-【了解】算法概念和小结_.mp4 [4.5 MB]
04-【重要】正则search的用法_.mp4 [24.6 MB]
11-【了解】1课堂答疑死锁探讨_.mp4 [28.7 MB]
09-【重要】匹配多个字符_.mp4 [26.9 MB]
14-【了解】1分组相关_.mp4 [48.9 MB]
06-【重要】匹配单个字符1_.mp4 [17.4 MB]
02-【了解】上下文管理器和生成器复习_.mp4 [73.5 MB]
16-【了解】正则表达式综合复习_.mp4 [83.9 MB]
23-【重要】时间复杂度的计算规则_.mp4 [21.9 MB]
28-【了解】2线性结构存储_.mp4 [30.1 MB]
11-【了解】2课堂答疑-锁的范围_.mp4 [6.2 MB]
29-【了解】下午课程小结_.mp4 [10.3 MB]
28-【了解】1数据存储-线性结构和非线性结构_.mp4 [13.6 MB]
03-【重要】正则表达是概念-思路分析_.mp4 [18.8 MB]
📁 📁 day01-面向对象基础
07-【了解】第1部分小结_.mp4 [1.1 MB]
08-【了解】识别类和对象-程序员角度_.mp4 [17.7 MB]
22-【了解】2魔法方法str-代码实现_.mp4 [18.7 MB]
27-【重要】1需求分析-实现思路-代码分析_.mp4 [31.1 MB]
27-【重要】3需求分析-实现思路-代码实现_.mp4 [25.6 MB]
24-【了解】1魔法方法del-思路分析_.mp4 [27.9 MB]
04-【重要】面向对象概念_.mp4 [37.1 MB]
25-【了解】魔法方法小结_.mp4 [7.1 MB]
06-【重要】面向对象三大特性_.mp4 [32.0 MB]
14-【重要】在类的内部通过self关键字获取属性_.mp4 [13.9 MB]
12-【重要】第2部分小结_.mp4 [14.1 MB]
28-今天内容梳理小结_.mp4 [36.3 MB]
21-【了解】中午课程回顾_.mp4 [38.2 MB]
26-【了解】1减肥小案例-思路分析_.mp4 [18.6 MB]
10-【重要】self功能演示-为什么需要self_.mp4 [35.1 MB]
18-【重要】有参init方法-代码实现_.mp4 [9.7 MB]
03-【了解】面向过程概念_.mp4 [18.8 MB]
09-【重要】类和对象-self关键字_.mp4 [57.9 MB]
16-【重要】无参init方法-思路分析_.mp4 [28.9 MB]
25-【了解】2魔法方法del-代码实现_.mp4 [20.2 MB]
01-【了解】课程总体说明_.mp4 [12.1 MB]
26-【了解】2减肥小案例-思路分析_.mp4 [12.2 MB]
27-【重要】2需求分析-实现思路-代码实现_.mp4 [23.1 MB]
23-【了解】课堂答疑如何找bug_.mp4 [11.0 MB]
11-【重要】self关键字作用-类内部调用方法_.mp4 [22.5 MB]
19-【重要】中午课程小结_.mp4 [15.7 MB]
17-【重要】无参init方法-代码实现_.mp4 [13.1 MB]
22-【了解】1魔法方法str-思路分析_.mp4 [18.9 MB]
20-【了解】init函数返回值-课堂答疑_.mp4 [32.9 MB]
02-【了解】课程要求_.mp4 [30.2 MB]
15-【了解】第3部分小结_.mp4 [11.1 MB]
05-【了解】面向对象和过程小结_.mp4 [9.8 MB]
29-作业说明_.mp4 [33.6 MB]
13-【重要】在类的外部设置获取属性_.mp4 [26.0 MB]
📁 📁 day09-数据结构和算法
17-【重要】二叉树广度优先遍历.wmv [29.5 MB]
22-【重要】小结.wmv [61.9 MB]
04-【了解】复习快速排序.wmv [72.1 MB]
16-【重要】1二叉树广度优先-思路分析.wmv [22.0 MB]
08-【了解】满二叉树-平衡二叉树-二次排序树.wmv [65.1 MB]
01-【了解】复习链表.wmv [65.9 MB]
10-【了解】树的应用场景-二叉树的性质.wmv [52.2 MB]
03-【了解】复习插入.wmv [23.7 MB]
15-【了解】模拟队列操作.wmv [17.1 MB]
06-【重要】04二分查找-非递归代码实现.wmv [14.7 MB]
13-【了解】广度优先深度优先.wmv [12.7 MB]
21-【重要】递归调用-课堂答疑.wmv [27.3 MB]
18-【重要】先序列中序后序-遍历.wmv [33.6 MB]
12-【了解】2树的概念.wmv [69.7 MB]
11-【重要】定义树类和节点类.wmv [26.9 MB]
06-【重要】03二分查找-非递归思路分析.wmv [41.9 MB]
06-【重要】01二分查找-概念和思路分析.wmv [70.8 MB]
06-【重要】02二分查找-代码实现.wmv [23.5 MB]
02-【了解】复习冒泡和选择.wmv [36.4 MB]
05-【重要】快速排序算稳定性.wmv [35.3 MB]
20-【重要】2先序列中序后序-代码实现.wmv [21.9 MB]
14-【重要】广度优先插节点2.wmv [29.0 MB]
19-【重要】1先序列中序后序-代码思路.wmv [19.3 MB]
14-【重要】广度优先插节点1.wmv [25.8 MB]
12-【了解】1复习二分查找.wmv [64.4 MB]
07-【了解】树的概念.wmv [39.8 MB]
09-【了解】树的顺序存储和链式存储优劣对比.wmv [32.5 MB]
📁 📁 day06-多任务编程下
08-【重要】创建守候线程_.mp4 [10.8 MB]
19-【了解】进程和线程的对比_.mp4 [12.1 MB]
04-【重要】多线程边代码边音乐_.mp4 [16.8 MB]
22-【重要】自定义上下文管理器-代码实现_.mp4 [21.6 MB]
05-【重要】多线程带参数边代码边音乐_.mp4 [17.6 MB]
17-【了解】中午课程回顾_.mp4 [59.4 MB]
18-【了解】有关进程资源搭建-进程切换复习_.mp4 [47.9 MB]
03-【了解】2线程的概念_.mp4 [6.0 MB]
25-【重要】通过生成器推导式方式创建生成器_.mp4 [23.8 MB]
27-【重要】2生成器应用场景-数据迭代器-代码实现_.mp4 [28.9 MB]
13-【重要】线程全局变量不安全-实验证明_.mp4 [14.9 MB]
28-【了解】小结和作业说明_.mp4 [8.3 MB]
03-【了解】1线程概念_.mp4 [10.5 MB]
26-【重要】2yield关键字产生生成器-思路分析_.mp4 [11.8 MB]
14-【了解】线程注意问题-小结_.mp4 [13.6 MB]
09-【重要】课堂答疑-设置守候线程函数-两个线程测量不一致_.mp4 [5.8 MB]
02-【了解】多进程-复习_.mp4 [37.9 MB]
23-【了解】2小结with语句和上下文管理器_.mp4 [7.1 MB]
27-【重要】1生成器应用场景-数据迭代器-思路分析_.mp4 [98.8 MB]
23-【了解】1注意enter返回self_.mp4 [59.4 MB]
15-【重要】1线程锁-思路分析_.mp4 [19.5 MB]
01-【了解】客户端和服务器通讯流程复习_.mp4 [95.5 MB]
21-【重要】自定义上下文管理器-思路分析_.mp4 [40.3 MB]
11-【拓展】操作系统如何加持代码-建立执行环境_.mp4 [92.7 MB]
26-【重要】1yield关键字产生生成器-思路分析_.mp4 [37.4 MB]
12-【重要】线程全局变量不安全-思路分析_.mp4 [40.3 MB]
15-【重要】2线程锁-代码实现_.mp4 [13.4 MB]
07-【重要】主线程等待子线程结束以后再结束_.mp4 [15.0 MB]
06-【重要】子线程被cup随机调度_.mp4 [45.1 MB]
20-【了解】with语句_.mp4 [46.7 MB]
16-【死锁】概念-现象演示_.mp4 [16.1 MB]
10-【重要】线程之间共享内存变量_.mp4 [26.2 MB]
24-【重要】生成器概念-通过生成器推导式方式_.mp4 [45.5 MB]
📁 📁 day08-数据结构和算法
09-【重要】指定位置添加结点-思路分析_.mp4 [30.4 MB]
18-【重要】2选择排序-代码分析_.mp4 [29.1 MB]
18-【重要】3选择排序-代码实现_.mp4 [12.1 MB]
23-【重要】4快速排序-递归调用分析_.mp4 [27.9 MB]
07_【重要】2链表遍历-课堂答疑_.mp4 [12.8 MB]
04-【重要】2结点类和链表类框架搭建-小结_.mp4 [16.5 MB]
08-【重要】头部插入结点_.mp4 [32.1 MB]
11-【重要】课堂答疑-头指针和头结点-代码和图转换_.mp4 [41.5 MB]
04-【重要】1结点类和链表类框架搭建_.mp4 [52.8 MB]
23-【重要】3快速排序-实现思路分析2_.mp4 [43.7 MB]
17-【重要】4冒泡排序-稳定性O_n平方_.mp4 [16.3 MB]
07_【重要】1链表遍历_.mp4 [13.2 MB]
21-【重要】4插入法-文档性_.mp4 [7.9 MB]
03-【了解】顺序存储和链表的对比_.mp4 [29.2 MB]
01-【了解】复习正则表达式_.mp4 [51.8 MB]
15-【了解】链表和顺序表对比_.mp4 [19.2 MB]
17-【重要】3冒泡排序-提前结束优化_.mp4 [9.5 MB]
05_【重要】链表是否为空_.mp4 [15.7 MB]
14-【重要】根据数据查找节点是否存在_.mp4 [12.5 MB]
16-【了解】算法稳定性_.mp4 [24.6 MB]
18-【重要】5选择排序-时间复杂度_.mp4 [15.3 MB]
08-【重要】尾部插入结点_.mp4 [26.5 MB]
20-【重要】3插入法-代码实现_.mp4 [18.8 MB]
17-【重要】1冒泡思想_.mp4 [28.0 MB]
02-【了解】复数据结构概念篇_.mp4 [48.9 MB]
13-【重要】代码调试串讲-图和代码_.mp4 [90.7 MB]
17-【重要】3冒泡排序-代码分析_.mp4 [11.9 MB]
19-【重要】1插入法-思想_.mp4 [19.1 MB]
23-【重要】5快速排序-代码实现_.mp4 [41.9 MB]
18-【重要】4选择排序-课堂答疑_.mp4 [11.0 MB]
12-【重要】删除结点-思路分析和代码调试_.mp4 [57.0 MB]
22-【重要】2快速排序-实现思路分析_.mp4 [27.7 MB]
22-【重要】1快速排序-思路分析_.mp4 [25.6 MB]
17-【重要】2冒泡排序-代码分析_.mp4 [45.4 MB]
18-【重要】1选择排序-思想_.mp4 [21.7 MB]
06_【重要】求链表长度_.mp4 [18.8 MB]
19-【重要】2插入法-代码分析_.mp4 [27.0 MB]
10-【重要】指定位置添加结点-代码实现_.mp4 [29.5 MB]
📁 📁 day03-学生管理系统
07-【重要】学生类-代码实现_.mp4 [21.6 MB]
24-【重要】浅拷贝和深拷贝-慢动作_.mp4 [13.8 MB]
02_【重要】复习-多态_类方法属性-静态方法_.mp4 [35.9 MB]
23-【重要】浅拷贝拷贝不可变类型-相当于引用赋值操作_.mp4 [3.4 MB]
06-【重要】学生类-思路分析_.mp4 [14.4 MB]
03_【重要】作业练习_.mp4 [70.0 MB]
18-【重要】初始化-代码实现_.mp4 [37.6 MB]
17-【重要】初始化-思路分析_.mp4 [55.6 MB]
08-【了解】学生管理类init_.mp4 [26.6 MB]
20-【重要】引用赋值_.mp4 [90.0 MB]
11-【了解】添加和显示所有学员_.mp4 [43.3 MB]
04-【了解】学生管理系统-基本功能和需求测试_.mp4 [88.4 MB]
21-【重要】python对变量的封装到位_.mp4 [20.5 MB]
12-【了解】删除学员_.mp4 [24.8 MB]
01_【了解】复习-封装继承_.mp4 [55.6 MB]
16-【重要】保存学员-代码实现_.mp4 [28.3 MB]
20-【重要】回调函数本质-函数入口地址做函数参数_.mp4 [28.1 MB]
14-【了解】查询某一个同学_.mp4 [20.4 MB]
13-【了解】中午课程回顾_.mp4 [69.5 MB]
05-【了解】学生管理系统-实现思路分析_.mp4 [13.3 MB]
16-【重要】课堂答疑列表推导式_.mp4 [5.0 MB]
09-【了解】学生管理类-显示界面_.mp4 [21.5 MB]
10-【了解】学生管理类-搭建框架_.mp4 [70.1 MB]
15-【重要】保存学员-思路分析_.mp4 [27.6 MB]
25-2【重要】深拷贝-拷贝不可变类型-返回引用_.mp4 [18.7 MB]
19-【重要】回调-解耦合_.mp4 [43.1 MB]
25-3【重要】深拷贝作用举例子_.mp4 [11.1 MB]
25-1【重要】深拷贝-拷贝可变类型-所有层全copy_.mp4 [16.1 MB]
22-【重要】浅拷贝拷贝可变类型-只拷贝第1层_.mp4 [33.2 MB]
📁 阶段15-AI智慧交通项目实战
📁 📁 02-yoloV8
01-YOLO发展_.mp4 [12.3 MB]
03-V8的使用_.mp4 [55.8 MB]
02-V8简介_.mp4 [18.4 MB]
05-streamlit的实现_.mp4 [64.2 MB]
04-效果展示_.mp4 [21.3 MB]
📁 📁 03-车流量统计
08-sort算法实现1 _.mp4 [87.1 MB]
02-多目标跟踪算法_.mp4 [63.6 MB]
01-车流量统计思想_.mp4 [26.6 MB]
03-sort和deepsort算法_.mp4 [29.4 MB]
11-deepsort算法跟踪_.mp4 [22.3 MB]
06-卡尔曼滤波思想_.mp4 [88.3 MB]
04-KM算法_.mp4 [36.3 MB]
05-卡尔曼滤波_.mp4 [67.0 MB]
10-sort算法实现跟踪_.mp4 [95.2 MB]
07-卡尔曼滤波实践_.mp4 [88.8 MB]
09-sort算法实现2_.mp4 [50.9 MB]
📁 📁 04-车道线检测
03-内容回顾_.mp4 [96.6 MB]
06-优化方法2_.mp4 [93.1 MB]
16-车辆偏离中心库里计算_.mp4 [31.8 MB]
12-车道线定位_.mp4 [60.1 MB]
07-相机较正流程_.mp4 [22.9 MB]
08-双目较正_.mp4 [18.0 MB]
15-车道线曲率_.mp4 [50.8 MB]
13-车道线拟合_.mp4 [79.9 MB]
01-车道线检测原理_.mp4 [53.7 MB]
05-优化方法_.mp4 [90.4 MB]
14-车道线填充_.mp4 [21.0 MB]
18-效果展示_.mp4 [11.4 MB]
02-相机坐标系转换_.mp4 [65.8 MB]
10-图像去畸变_.mp4 [22.6 MB]
11-车道线提取_.mp4 [46.2 MB]
09-相机较正实现_.mp4 [110.2 MB]
04-相机较正方法_.mp4 [98.1 MB]
17-车道线检测流程_.mp4 [26.1 MB]
📁 📁 01-opencv
13-边缘检测思想_.mp4 [76.5 MB]
10-透射变换_.mp4 [15.7 MB]
12-图像平滑方法_.mp4 [71.9 MB]
14-sobel边缘检测_.mp4 [17.9 MB]
09-图像旋转和仿射变换_.mp4 [37.5 MB]
06-绘制几何图像_.mp4 [38.2 MB]
08-图像缩放与平移_.mp4 [50.3 MB]
15-canny边缘检测_.mp4 [37.1 MB]
03-资料共享_.mp4 [6.3 MB]
11-图像噪声_.mp4 [12.4 MB]
07-图像加法_.mp4 [32.6 MB]
01-项目架构_.mp4 [15.9 MB]
04-opencv介绍_.mp4 [16.7 MB]
16-视频读写_.mp4 [42.8 MB]
02-项目构成_.mp4 [12.1 MB]
05-图像读写_.mp4 [29.3 MB]
17-opencv总结_.mp4 [52.1 MB]
📁 阶段1-python基础编程
📁 📁 day08
05-异常中的else.mp4 [13.6 MB]
07-异常练习.mp4 [23.4 MB]
23-学生管理系统--展示学员和退出程序.mp4 [35.3 MB]
10-给模块和功能起别名.mp4 [33.6 MB]
08-异常传递(异常穿透).mp4 [14.7 MB]
00-复习和作业讲解.mp4 [145.6 MB]
14-包的使用.mp4 [42.6 MB]
09-模块的导入方式.mp4 [43.7 MB]
21-学生管理系统--修改学员.mp4 [28.7 MB]
20-学生管理系统--删除学员.mp4 [46.5 MB]
17-学生管理系统框架搭建.mp4 [24.8 MB]
16-学生管理系统需求分析.mp4 [11.9 MB]
13-__all__的使用方法.mp4 [36.1 MB]
04-获取异常描述信息.mp4 [31.6 MB]
22-学生管理系统--查询学员.mp4 [40.2 MB]
03-捕获指定类型异常.mp4 [52.4 MB]
15-模块中演示代码的书写位置.mp4 [27.5 MB]
19-学生管理系统--添加学员.mp4 [63.6 MB]
01-异常的介绍.mp4 [32.2 MB]
24-今日总结.mp4 [39.8 MB]
02-异常捕获体验.mp4 [25.1 MB]
12-自定义模块.mp4 [28.2 MB]
18-学生管理系统函数抽取.mp4 [59.4 MB]
06-异常中的finally.mp4 [36.8 MB]
📁 📁 day02
20-while循环语句详解.mp4 [33.7 MB]
12-对立条件分支语句.mp4 [26.2 MB]
00-复习和反馈.mp4 [148.0 MB]
17-猜拳游戏.mp4 [55.2 MB]
05-赋值运算符.mp4 [43.7 MB]
02-数据类型转换补充.mp4 [70.3 MB]
01-f-string字符串.mp4 [65.1 MB]
16-分支语句的嵌套.mp4 [33.1 MB]
13-debug调试.mp4 [22.7 MB]
14-多条件分支语句.mp4 [60.6 MB]
11-单条件分支语句.mp4 [18.5 MB]
19-循环语句的体验.mp4 [18.4 MB]
21-今日总结.mp4 [21.4 MB]
04-算数运算符.mp4 [45.7 MB]
09-上午知识回顾.mp4 [71.9 MB]
10-三种流程语句介绍.mp4 [12.7 MB]
18-三目运算符.mp4 [17.8 MB]
08-逻辑运算符.mp4 [24.3 MB]
03-今日学习内容.mp4 [13.8 MB]
15-练习讲解.mp4 [16.8 MB]
06-比较运算符.mp4 [27.1 MB]
07-字符串大小比较.mp4 [72.9 MB]
📁 📁 day05
06-列表的嵌套.mp4 [36.3 MB]
20-容器的公共运算符.mp4 [64.2 MB]
15-字典的操作--查.mp4 [38.0 MB]
04-列表的反转和排序.mp4 [30.8 MB]
02-在列表中删除数据时会影响原有数据的索引值.mp4 [26.1 MB]
05-解决代码实现中的小问题.mp4 [33.6 MB]
19-字典的遍历方法.mp4 [18.1 MB]
21-容器的公共函数.mp4 [51.8 MB]
08-推导式练习讲解.mp4 [17.5 MB]
03-列表的修改操作.mp4 [9.7 MB]
01-列表的删除操作.mp4 [63.2 MB]
09-元组定义.mp4 [34.6 MB]
17-字典的修改操作.mp4 [19.0 MB]
11-上午知识回顾.mp4 [36.4 MB]
14-字典的定义.mp4 [39.1 MB]
10-元组的特性.mp4 [30.1 MB]
07-列表的推导式.mp4 [38.7 MB]
18-字典的删除操作.mp4 [22.5 MB]
12-元组的常见操作(仅有查询).mp4 [30.7 MB]
13-set集合的使用方法.mp4 [60.4 MB]
00-复习和作业讲解.mp4 [111.6 MB]
22-今日总结.mp4 [15.5 MB]
16-字典的增的操作.mp4 [23.0 MB]
📁 📁 day04
03-多种引号嵌套使用.mp4 [25.7 MB]
12-replace方法的使用.mp4 [25.8 MB]
16-字符串方法补充2.mp4 [53.9 MB]
02-字符串的定义.mp4 [27.0 MB]
01-容器类型介绍.mp4 [19.6 MB]
11-上午知识回顾.mp4 [57.7 MB]
04-字符串的下标.mp4 [38.1 MB]
08-find()方法的使用.mp4 [41.1 MB]
14-字符串的应用.mp4 [41.0 MB]
18-列表的遍历.mp4 [15.3 MB]
07-字符串切片的省略模式.mp4 [38.6 MB]
09-index方法的使用.mp4 [41.3 MB]
05-字符串切片.mp4 [65.3 MB]
20-列表的查的操作.mp4 [31.3 MB]
21-今日总结.mp4 [30.0 MB]
15-字符串方法补充1.mp4 [47.2 MB]
17-列表的定义.mp4 [32.2 MB]
06-切片练习讲解.mp4 [12.2 MB]
13-split方法的使用.mp4 [36.1 MB]
19-列表的增的操作.mp4 [43.4 MB]
10-字符串查找练习讲解.mp4 [22.7 MB]
00-复习和作业.mp4 [89.5 MB]
📁 📁 day03
11-for循环的应用--输出矩形.mp4 [24.0 MB]
20-报数小游戏.mp4 [23.0 MB]
14-上午知识回顾.mp4 [50.7 MB]
18-break和continue的注意事项.mp4 [54.0 MB]
13-for循环的应用--九九乘法表.mp4 [28.9 MB]
19-循环中的else语句.mp4 [54.7 MB]
08-for循环的使用.mp4 [37.1 MB]
10-for循环配合range函数使用.mp4 [18.6 MB]
22-今日总结.mp4 [15.5 MB]
05-循环嵌套的应用--打印矩形.mp4 [29.3 MB]
01-while应用-计算1-100累加和.mp4 [31.8 MB]
21-猜数游戏.mp4 [33.0 MB]
04-循环嵌套的介绍.mp4 [58.8 MB]
12-for循环的应用--输出直角三角形.mp4 [21.8 MB]
17-continue的使用.mp4 [28.8 MB]
15-for循环的应用--打印等腰三角形.mp4 [34.8 MB]
06-循环嵌套的应用--打印三角形.mp4 [31.3 MB]
07-猜拳游戏的优化.mp4 [64.9 MB]
02-练习讲解.mp4 [30.0 MB]
03-while应用-计算1-100的偶数累加和.mp4 [13.6 MB]
00-复习和反馈.mp4 [90.1 MB]
16-break的使用.mp4 [20.1 MB]
09-range函数的使用.mp4 [37.6 MB]
📁 📁 day07
17-os模块的使用.mp4 [50.9 MB]
05-文件的介绍和文件读取体验.mp4 [15.4 MB]
00-复习和作业讲解.mp4 [172.7 MB]
16-相对路径和绝对路径.mp4 [32.3 MB]
06-文件的读取操作.mp4 [72.3 MB]
13-文件读写模式.mp4 [16.6 MB]
07-文件读取练习.mp4 [14.8 MB]
10-文件的追加操作.mp4 [41.7 MB]
18-今日总结.mp4 [13.1 MB]
04-递归(了解).mp4 [69.4 MB]
11-文件备份案例.mp4 [27.4 MB]
01-今日课程内容.mp4 [17.5 MB]
14-文件读写模式的加强模式练习.mp4 [58.6 MB]
12-文件备份案例--字节型文件备份.mp4 [49.0 MB]
09-文件的写入操作.mp4 [56.4 MB]
08-上午知识回顾.mp4 [45.9 MB]
15-字符集的了解.mp4 [52.5 MB]
02-lambda表达式.mp4 [59.8 MB]
03-lambda练习讲解.mp4 [34.8 MB]
📁 📁 day01
03-python语言介绍.mp4 [81.6 MB]
05-python解释器的介绍.mp4 [56.9 MB]
共享文件软件使用.mp4 [26.0 MB]
12-上午知识回顾.mp4 [54.9 MB]
00-课前须知.mp4 [45.1 MB]
16-多占位符的格式化输出.mp4 [36.4 MB]
13-变量的数据类型.mp4 [35.5 MB]
11-变量的使用.mp4 [23.9 MB]
20-input接收的类型都是字符串类型.mp4 [22.1 MB]
19-输入函数.mp4 [24.7 MB]
07-使用pycharm创建工程.mp4 [14.6 MB]
21-数据类型转换.mp4 [38.4 MB]
18-print函数详解.mp4 [22.8 MB]
01-计算机的介绍.mp4 [49.0 MB]
15-单占位符的格式化输出.mp4 [29.7 MB]
04-编译型语言和解释型语言介绍.mp4 [20.8 MB]
08-pycharm的基础配置.mp4 [24.3 MB]
22-今日总结.mp4 [50.6 MB]
06-pycharm的介绍和安装.mp4 [40.8 MB]
17-占位符的精度控制问题.mp4 [31.9 MB]
14-标识符和关键字.mp4 [56.8 MB]
10-pycharm使用中的小问题.mp4 [19.4 MB]
02-编程语言介绍.mp4 [19.8 MB]
09-python中的注释.mp4 [46.6 MB]
📁 📁 day06
09-在函数体内部嵌套函数的调用.mp4 [19.9 MB]
18-形参-缺省参数.mp4 [19.9 MB]
04-函数的说明文档.mp4 [24.5 MB]
03-函数定义的注意事项.mp4 [19.9 MB]
00-作业及复习.mp4 [135.5 MB]
17-形参-位置参数.mp4 [7.4 MB]
15-实参-关键字参数赋值.mp4 [28.3 MB]
12-上午知识回顾.mp4 [42.9 MB]
24-今日总结.mp4 [12.3 MB]
10-函数执行流程说明.mp4 [25.0 MB]
19-形参-位置不定长参数.mp4 [32.8 MB]
06-函数的返回值.mp4 [24.9 MB]
20-形参-关键字不定长参数.mp4 [52.5 MB]
16-实参加强练习讲解.mp4 [10.4 MB]
23-可变数据类型和不可变数据类型.mp4 [42.7 MB]
08-global关键字.mp4 [51.5 MB]
21-组包和拆包.mp4 [22.4 MB]
11-函数的参数和返回值传递.mp4 [20.5 MB]
13-函数返回值加强.mp4 [25.5 MB]
14-实参-位置参数.mp4 [14.4 MB]
05-函数的参数.mp4 [21.3 MB]
22-引用.mp4 [67.5 MB]
07-函数的作用域.mp4 [30.3 MB]
01-函数的介绍.mp4 [21.7 MB]
02-函数的简单使用.mp4 [21.7 MB]
📁 阶段14-亿图人脸支付项目
📁 📁 04-人脸识别
02.模型训练_.mp4 [71.7 MB]
06.人脸矫正_.mp4 [67.5 MB]
01.内容回顾_.mp4 [14.9 MB]
08.可视化_.mp4 [172.7 MB]
05.代码结构_.mp4 [32.3 MB]
10.人脸支付项目总结_.mp4 [40.7 MB]
04.模型集成_.mp4 [63.3 MB]
07.属性获取_.mp4 [38.7 MB]
03.模型使用_.mp4 [162.8 MB]
09.模型部署_.mp4 [46.2 MB]
📁 📁 02-人脸姿态
01.内容回顾_.mp4 [35.0 MB]
04.模型预测流程_.mp4 [120.8 MB]
09.模型训练_.mp4 [93.8 MB]
07.数据增强_.mp4 [147.1 MB]
08.模型构建_.mp4 [95.0 MB]
05.人脸姿态概述_.mp4 [55.7 MB]
06.数据集加载_.mp4 [47.7 MB]
02.模型训练结果_.mp4 [39.3 MB]
03.模型预测_.mp4 [38.2 MB]
10. 内容总结_.mp4 [11.3 MB]
📁 📁 03-人脸多任务
05.模型构建_.mp4 [16.8 MB]
03.数据加载_.mp4 [153.0 MB]
09.数据获取_.mp4 [40.3 MB]
07.模型预测_.mp4 [45.1 MB]
04.数据增强_.mp4 [30.8 MB]
10.模型构建_.mp4 [89.9 MB]
12.内容总结_.mp4 [12.0 MB]
08.人脸识别_.mp4 [73.1 MB]
01.内容回顾_.mp4 [17.0 MB]
06.模型训练_.mp4 [82.1 MB]
11.arcface_.mp4 [62.4 MB]
02.人脸多任务_.mp4 [114.8 MB]
📁 📁 01-人脸检测
04.验证数据集_.mp4 [87.1 MB]
09.训练流程_.mp4 [85.5 MB]
02.视频读写_.mp4 [62.2 MB]
05.数据集获取_.mp4 [73.1 MB]
07.参数配置_.mp4 [54.6 MB]
03.人脸检测概述_.mp4 [47.1 MB]
08.训练策略_.mp4 [31.2 MB]
11.内容总结_.mp4 [13.5 MB]
01.内容回顾_.mp4 [26.8 MB]
10.模型训练_.mp4 [36.4 MB]
06.模型构建_.mp4 [144.7 MB]
📁 阶段5-金融风控项目与数据挖掘
📁 📁 day02
14_决策树辅助构建规则案例说明_.mp4 [13.4 MB]
11_建模流程概述_Y标签确定观察期表现期_.mp4 [14.8 MB]
04_Vintage报表SQL实现_.mp4 [32.8 MB]
06_信贷审批流程介绍_.mp4 [24.8 MB]
08_建模流程概述_评分卡介绍和模型开发前准备_.mp4 [13.4 MB]
13_建模流程概述_特征构造_.mp4 [27.3 MB]
01_内容回顾_.mp4 [26.0 MB]
03_Vintage报表概念介绍_.mp4 [16.5 MB]
12_模型建模概述_Y标签确定以及样本选取问题说明_.mp4 [13.2 MB]
15_今日重点内容回顾_.mp4 [30.2 MB]
09_建模流程概述_Y标签设计_.mp4 [11.0 MB]
02_通过率表和放款表_.mp4 [30.9 MB]
05_Vintage报表问题说明&催收报表说明_.mp4 [31.7 MB]
07_业务重点回顾_.mp4 [10.4 MB]
10_建模流程概述_Y标签阈值确定_.mp4 [11.1 MB]
📁 📁 day03
16_今日小结_.mp4 [12.1 MB]
03_业务规则挖掘_代码实现2_.mp4 [22.9 MB]
13_特征变换小结_.mp4 [15.2 MB]
01_内容回顾_.mp4 [26.5 MB]
14_特征筛选_单特征筛选_.mp4 [16.3 MB]
08_风控特征衍生问题强调_.mp4 [8.4 MB]
05_特征构造_未来信息介绍_.mp4 [11.5 MB]
06_特征构造_时序特征的特征构造_.mp4 [21.4 MB]
12_特征变化_WOE编码代码实现_.mp4 [21.6 MB]
15_单特征筛选小结_.mp4 [9.8 MB]
07_特征衍生小结_.mp4 [22.6 MB]
10_特征变换_卡方分箱_.mp4 [38.1 MB]
09_特征变换_分箱介绍_.mp4 [20.4 MB]
02_业务规则挖掘_代码实现1_.mp4 [32.4 MB]
04_特征构造_特征工程之前的准备_.mp4 [15.4 MB]
11_特征变换_WOE编码_.mp4 [14.5 MB]
📁 📁 day06
02_使用toad梳理评分卡开发流程_.mp4 [17.0 MB]
08_通用套路说明_.mp4 [9.4 MB]
05_特征筛选&模型训练_.mp4 [19.9 MB]
10_使用SMOTE做过采样_.mp4 [45.1 MB]
01_内容回顾_.mp4 [13.5 MB]
06_模型训练&得到评分卡_.mp4 [25.7 MB]
03_数据加载&单特征筛选&分箱计算_.mp4 [24.5 MB]
14_异常检测_孤立森林应用场景_.mp4 [24.1 MB]
11_样本不均衡问题小结_.mp4 [7.9 MB]
07_模型报告和生成评分卡代码说明_.mp4 [29.4 MB]
13_异常点检测_孤立森林_.mp4 [30.3 MB]
15_今日内容小结_.mp4 [35.9 MB]
09_样本不均衡问题的处理_classweight_.mp4 [28.0 MB]
12_异常点检测_LOF_.mp4 [17.3 MB]
04_计算PSI&再次使用IV进行过滤_.mp4 [24.8 MB]
📁 📁 实战
06_第六组答辩_.mp4 [11.6 MB]
03_第三组答辩_.mp4 [20.1 MB]
02_版本控制工具简介_.mp4 [23.9 MB]
06_问题说明_.mp4 [63.1 MB]
03_项目仓库介绍_.mp4 [32.5 MB]
04_.mp4 [31.9 MB]
05_git操作_拉分支冲突解决_.mp4 [28.2 MB]
05_第五组答辩_.mp4 [49.2 MB]
01_第一组答辩_.mp4 [21.3 MB]
02_第二组答辩_.mp4 [50.6 MB]
04_git操作_clone&commit&push_.mp4 [36.8 MB]
01_内容回顾_.mp4 [17.3 MB]
📁 📁 day05
05_LightGBM原理_GOSS_EFB_Leafwise生长策略_.mp4 [20.7 MB]
06_LightGBM的API_学习率和早停_.mp4 [9.6 MB]
08_LightGBM的API_早停的影响_.mp4 [30.7 MB]
02_评分卡评分转换_.mp4 [37.7 MB]
07_LightGBM的API_学习率大小影响代码实现_.mp4 [44.2 MB]
01_昨日内容回顾(2)_.mp4 [26.9 MB]
09_LightGBM的API_自定义损失函数(了解)_.mp4 [26.7 MB]
10_LightGBM特征重要性做交叉验证筛选特征_.mp4 [11.8 MB]
03_LightGBM原理_基于直方图的特征分裂_.mp4 [16.2 MB]
12_LightGBM评分卡_.mp4 [61.9 MB]
11_LightGBM_按时间交叉验证做特征筛选_.mp4 [46.4 MB]
04_LightGBM原理_直方图特征分裂示例代码说明_.mp4 [20.5 MB]
📁 📁 day07
09_shap代码实现_.mp4 [21.8 MB]
10_GBDT特征衍生介绍_.mp4 [43.2 MB]
04_拒绝推断方法_模糊展开介绍&硬截断实现_.mp4 [25.7 MB]
13_整体回顾_01_.mp4 [60.2 MB]
06_拒绝推断完成_.mp4 [11.2 MB]
07_模型可解释性介绍_.mp4 [12.5 MB]
03_拒绝推断方法_硬截断&加权_.mp4 [7.8 MB]
08_shap介绍_.mp4 [10.1 MB]
05_拒绝推断方法_模糊展开&重新加权代码实现_.mp4 [22.8 MB]
14_整体回顾_02_.mp4 [14.9 MB]
02_拒绝推断的概念_.mp4 [15.5 MB]
11_GBDT特征衍生代码实现_.mp4 [34.0 MB]
12_GBDT特征交叉小结_.mp4 [17.3 MB]
01_昨日内容回顾_.mp4 [20.2 MB]
📁 📁 day04
09_特征监控_.mp4 [14.3 MB]
11_逻辑回归评分卡代码实现_.mp4 [19.4 MB]
02_多特征筛选_星座特征和Boruta_.mp4 [15.1 MB]
16_模型报告计算_KS值说明_.mp4 [19.6 MB]
07_多特征筛选_RFE和L1代码实现_.mp4 [24.8 MB]
06_多特征筛选_其它筛选方式和VIF问题说明_.mp4 [10.7 MB]
01_昨日内容回顾_.mp4 [20.3 MB]
14_评分卡训练过程顺序梳理_.mp4 [23.0 MB]
13_逻辑回归评分卡问题说明_.mp4 [24.8 MB]
03_多特征筛选_星座特征和Boruta代码实现_.mp4 [21.5 MB]
04_多特征筛选_方差膨胀系数VIF_.mp4 [17.2 MB]
15_模型报告计算_1_.mp4 [26.7 MB]
17_内容回顾_.mp4 [25.5 MB]
05_多特征筛选_方差膨胀系数代码实现_.mp4 [10.5 MB]
10_逻辑回归评分卡介绍_.mp4 [17.7 MB]
08_逻辑回归评分卡_如何评价模型好坏_.mp4 [15.8 MB]
12_使用lightgbm特征重要性行进特征筛选_.mp4 [33.4 MB]
📁 📁 day01
10_业务指标计算_回收账单逾期情况统计_.mp4 [19.7 MB]
02_信贷产品简介_.mp4 [10.0 MB]
03_金融风控相关术语介绍_.mp4 [7.9 MB]
17_风控报表_通过率计算_.mp4 [32.1 MB]
18_风控报表_内容小结_.mp4 [30.5 MB]
09_业务指标计算_计算入催率_.mp4 [19.1 MB]
13_风控报表_各阶段转化率_表关联关系说明_.mp4 [23.8 MB]
07_业务指标计算_90+逾期情况计算_.mp4 [27.2 MB]
16_风控报表_各阶段转化率计算_.mp4 [30.2 MB]
08_业务指标计算_数据可视化_.mp4 [9.3 MB]
11_风控业务运行介绍_.mp4 [14.7 MB]
05_业务指标计算案例_数据处理类型转换_.mp4 [31.8 MB]
04_业务指标计算案例_数据介绍_.mp4 [11.3 MB]
06_业务指标计算案例_创建逾期字段_.mp4 [14.9 MB]
01_信贷风险介绍_.mp4 [13.7 MB]
12_风控报表_表结构介绍_.mp4 [27.2 MB]
14_风控报表_各阶段转化率_计算基础字段完成_.mp4 [16.1 MB]
15_风控报表_各阶段转化率_统计每个用户的各阶段状态_.mp4 [16.7 MB]
📁 阶段7-自然语言处理基础
📁 📁 day10_迁移学习案例实战
24-【重要】mask任务-模型训练-代码移植_.mp4 [27.7 MB]
23-【重要】mask任务-模型训练-思路分析_.mp4 [35.7 MB]
15-【重要】mask任务-任务识别_.mp4 [11.5 MB]
18-【重要】mask任务-数据处理-过滤器-代码实现_.mp4 [17.9 MB]
21-【重要】mask任务-模型-思路分析_.mp4 [43.6 MB]
06-【重要】中文分类-数据处理-数据二次处理-代码实现_.mp4 [54.6 MB]
32-【重要】作业和小结_.mp4 [9.7 MB]
04-【了解】中文分类-数据处理-dataset操作-代码编写_.mp4 [23.5 MB]
13-【了解】中文分类-小结和习题_.mp4 [9.6 MB]
26-【了解】mask任务-小结_.mp4 [5.4 MB]
22-【重要】mask任务-模型-代码实现_.mp4 [35.3 MB]
01-【了解】上一次课程复习_.mp4 [40.8 MB]
30-【重要】nsp任务-二次数据处理-思路分析_.mp4 [38.2 MB]
10-【了解】中文分类-模型训练-思路分析_.mp4 [31.2 MB]
09-【重要】中文分类-搭建迁移学习模型-调试_.mp4 [19.0 MB]
11-【了解】中文分类-模型训练-代码实现_.mp4 [75.8 MB]
29-【重要】nsp任务-数据处理-代码实现_.mp4 [61.6 MB]
12-【了解】中文分类-模型预测_.mp4 [60.3 MB]
16-【重要】mask任务-数据处理-过滤器_.mp4 [35.1 MB]
03-【了解】中文分类-数据处理-dataset操作_.mp4 [52.5 MB]
19-【重要】mask任务-数据处理-二次处理-思路分析_.mp4 [59.2 MB]
08-【重要】中文分类-搭建迁移学习模型-代码实现_.mp4 [20.4 MB]
14-【了解】中午课程回顾_.mp4 [77.7 MB]
20-【重要】mask任务-数据处理-二次处理-代码实现_.mp4 [52.5 MB]
25-【了解】mask任务-模型评估_.mp4 [26.1 MB]
27-【重要】nsp任务-任务识别_.mp4 [24.7 MB]
33-【答疑】nsp产生正负样本-mask数据103_.mp4 [30.7 MB]
07-【重要】中文分类-搭建迁移学习模型-思路分析_.mp4 [64.9 MB]
17-【重要】mask单词的特征是如何被表达出来的_.mp4 [28.2 MB]
31-【重要】nsp任务-二次数据处理-代码实现_.mp4 [21.0 MB]
05-【重要】中文分类-数据处理-数据二次处理-思路分析_.mp4 [92.8 MB]
28-【重要】nsp任务-数据处理-思路分析_.mp4 [71.3 MB]
02-【了解】中文分类-任务介绍-数据集介绍_.mp4 [51.4 MB]
📁 📁 day07_Transformer
21-【了解】编码器层-思路分析_.mp4 [38.0 MB]
19-【重要】子层连接结构-代码实现_.mp4 [41.7 MB]
11-【了解】前馈全连接层-思路分析_.mp4 [6.2 MB]
24-【了解】编码器-代码实现_.mp4 [16.5 MB]
31-【了解】作业和小结_.mp4 [18.5 MB]
02-【重要】复习transformer框架-添加位置特性-自注意力机制_.mp4 [67.6 MB]
12-【了解】前馈全连接层-代码实现_.mp4 [19.0 MB]
18-【重要】子层连接结构-思路分析_.mp4 [73.3 MB]
25-【了解】编码器层和编码器部分-小结和练习_.mp4 [20.8 MB]
09-【了解】多头注意力机制-代码调试_.mp4 [15.5 MB]
16-【答疑】batchnorm和layernorm的联系和区别_.mp4 [32.1 MB]
28-【了解】解码器-思路分析和代码实现_.mp4 [38.6 MB]
05-【重要】多头注意力机制-数据形状变化分析_.mp4 [22.5 MB]
06-【重要】多头注意力机制-代码数据形状分析_.mp4 [86.9 MB]
29-【重要】有关mask的作用_.mp4 [54.2 MB]
13-【重要】为什么要规范化层-代码分析_.mp4 [80.7 MB]
01-【了解】录制seq2seq训练函数-打样_.mp4 [52.5 MB]
15-【重要】规范化层-代码实现_.mp4 [26.0 MB]
22-【了解】编码器层-代码实现_.mp4 [18.8 MB]
04-【重要】多头注意力机制-概念-作用-结构图_.mp4 [44.8 MB]
07-【重要】多头注意力机制-代码疑难点讲解_.mp4 [79.6 MB]
27-【了解】解码器层-代码实现_.mp4 [44.3 MB]
26-【了解】解码器层-思路分析_.mp4 [56.2 MB]
30-【重要】有关如何使用中间语义张量C_.mp4 [5.3 MB]
08-【重要】多头注意力机制-代码实现_.mp4 [75.2 MB]
17-【了解】中午课程回顾_.mp4 [34.9 MB]
03-【重要】注意力机制中的1方向和2方向_.mp4 [49.2 MB]
14-【答疑】数据和权重参数要分开-权重参数的作用_.mp4 [16.7 MB]
20-【了解】有关残差连接的说明_.mp4 [39.0 MB]
10-【了解】多头注意力机制-小结_.mp4 [10.3 MB]
23-【了解】编码器-思路分析_.mp4 [44.5 MB]
📁 📁 day06_注意力机制seq2seq
13-【了解】transformer结构复习_.mp4 [50.8 MB]
24-【重要】自注意力计算规则-意义解读_.mp4 [58.6 MB]
23-【重要】自注意力计算规则_.mp4 [38.5 MB]
11-【重要】transformer小结和练习_.mp4 [8.9 MB]
18-【了解】位置编码器层-答疑广播机制_.mp4 [9.3 MB]
05-【了解】每个时间步的权重分布-制图_.mp4 [61.6 MB]
01-【重要】上一次课程复习_.mp4 [165.8 MB]
02-【重要】teacher-forcing概念和作用_.mp4 [58.2 MB]
27-【了解】小结和作业_.mp4 [8.7 MB]
16-【了解】位置编码器层-机理_.mp4 [47.6 MB]
12-【了解】中午课程复习_.mp4 [53.4 MB]
21-【了解】总结和练习_.mp4 [8.5 MB]
04-【了解】模型预测-业务函数串讲_.mp4 [62.0 MB]
09-【重要】记忆一遍transformer架构_.mp4 [31.3 MB]
25-【重要】注意力机制计算规则-代码实现_.mp4 [56.5 MB]
06-【实验】在gpu上训练seq2seq_.mp4 [66.7 MB]
19-【了解】位置编码器层-代码实现_.mp4 [48.9 MB]
20-【了解】位置编码器层-代码调试_.mp4 [22.4 MB]
08-【重要】transformer架构_.mp4 [33.3 MB]
15-【了解】位置编码器层-代码实现_.mp4 [17.8 MB]
07-【了解】transformer简介_.mp4 [61.6 MB]
10-【重要】transformer常见问题_.mp4 [27.6 MB]
14-【了解】位置编码器层-思路分析_.mp4 [45.9 MB]
26-【重要】注意力机制计算规则-mask权重分布-结果解读_.mp4 [53.0 MB]
17-【重要】位置编码器层-思路分析_.mp4 [137.9 MB]
03-【了解】模型预测-业务测试函数串讲_.mp4 [45.8 MB]
22-【了解】上三角矩阵和下三角矩阵-解码时防止模型看到未来信息_.mp4 [54.8 MB]
📁 📁 day08_fasttext分类-词向量迁移
08-【了解】fasttext概念-优势-安装_.mp4 [44.5 MB]
04-【了解】makemodel-思路分析_.mp4 [68.0 MB]
19-【复习】文本预处理其他_.mp4 [29.5 MB]
17-【复习】文本处理基本方法-张量表示_.mp4 [80.5 MB]
23-【复习】seq2seq案例复习_.mp4 [32.8 MB]
27-【了解】fasttext文本分类案例-数据处理_.mp4 [79.7 MB]
15-【了解】中午课程复习_.mp4 [30.1 MB]
07-【了解】复盘小结_.mp4 [95.4 MB]
01-【了解】编码部分-复习_.mp4 [92.9 MB]
26-【测试】同学测试演讲seq2seq_.mp4 [31.9 MB]
02-【了解】解码部分-复习_.mp4 [23.2 MB]
03-【了解】输出部分_.mp4 [23.8 MB]
20-【复习】rnn相关_.mp4 [26.3 MB]
06-【重要】词嵌入层为什么不c_.mp4 [40.4 MB]
05-【了解】makemodel-代码实现1_.mp4 [55.8 MB]
14-【了解】小结和练习_.mp4 [16.2 MB]
12【面试题】-霍夫曼树是如何被训练出来-构建联合概率-通过极大似然损失构建损失函数训练出来_.mp4 [48.9 MB]
09-【重要】层次softmax比普通softmax速度快-答案_.mp4 [36.3 MB]
10-【重要】层次softmax计算概率的栗子_.mp4 [10.6 MB]
22-【复习】人名分类器案例_.mp4 [48.1 MB]
16-【了解】fasttext模型常见面试题复习_.mp4 [25.6 MB]
28-【重要】fasttext文本分类案例-模型预测-思路分析_.mp4 [22.1 MB]
18-【复习】有关词向量技术体系演变的社会学思考_.mp4 [10.4 MB]
24-【复习】transformer架构_.mp4 [27.4 MB]
11-【重要】构建霍夫曼树_.mp4 [44.2 MB]
25-【测试】同学测试演讲seq2seq_.mp4 [24.8 MB]
21-【复习】gru和lstm-注意力机制_.mp4 [47.4 MB]
13-【面试图】负采样只更新一部分权重参数_.mp4 [44.3 MB]
📁 📁 day01_NLP概述-文本预处理上
01-【了解】nlp基础专业课前说明_.mp4 [50.2 MB]
06-【重要】jieba分词-三种分词模式_.mp4 [57.7 MB]
31-【重要】数据形状代码调试_.mp4 [10.7 MB]
29-【重要】nn-Embedding词向量数据形状变化_.mp4 [34.0 MB]
10-【了解】onehot概念-onehot生成词向量思路分析_.mp4 [56.3 MB]
02-【了解】NLP简介和发展史_.mp4 [23.1 MB]
22-【实验课】nlpbase资源包说明_.mp4 [20.5 MB]
14-【重要】word2vec-理念-用深度学习权重参数来模拟词向量_.mp4 [62.5 MB]
11-【答疑】保存了tokenizer分词器没有保存onehot编码的结果_.mp4 [3.5 MB]
25-【了解】fasttext训练参数调整_.mp4 [49.8 MB]
19-【实验课】配置pycharm连接远程服务器python环境_.mp4 [80.5 MB]
30-【答疑】语料单词个数和词向量单词个数大小关系_.mp4 [40.3 MB]
05-【了解】分词的概念和作用-jieba工具简介_.mp4 [15.5 MB]
12-【重要】onehot生成词向量代码实现_.mp4 [29.0 MB]
03-【了解】NLP应用场景-小结_.mp4 [69.7 MB]
07-【重要】jieba分词-用户自定义字典_.mp4 [30.6 MB]
13-【重要】onehot编码小结_.mp4 [14.8 MB]
27-【重要】词向量可视化需求分析_.mp4 [38.1 MB]
08-【了解】jieba分词-命名实体识别_.mp4 [7.7 MB]
24-【了解】fasttext查看单词词向量-查看临近词_.mp4 [54.9 MB]
26-【重要】word2vec和nn-Embedding区别和联系_.mp4 [23.4 MB]
23-【了解】fasttext训练词向量-处理数据-下载工具包_.mp4 [53.3 MB]
04-【了解】文本预处理的主要环节_.mp4 [79.3 MB]
18-【重要】word2vec-skipgram方式训练词向量原理_.mp4 [29.2 MB]
21-【实验课】经常遇到的问题_.mp4 [40.9 MB]
20-【实验课】配置pycharm连接远程服务器python环境-小结_.mp4 [27.6 MB]
12-【重要】onehot使用词向量_.mp4 [37.9 MB]
32【了解】今天作业-下一次课程内容_.mp4 [27.6 MB]
15-【重要】word2vec-cbow的词向量训练原理_.mp4 [38.6 MB]
09-【了解】词向标注-小结_.mp4 [39.6 MB]
17-【了解】中午课程回顾_.mp4 [71.7 MB]
28-【重要】词向量可视化代码实现_.mp4 [57.2 MB]
16-【重要】word2vec-cbow的词向量如何获取_.mp4 [14.4 MB]
28-【重要】词向量可视化代码串讲_.mp4 [24.1 MB]
📁 📁 day05_注意力机制seq2seq
23-【了解】编码器解码器-小结和练习_.mp4 [26.4 MB]
02-【了解】案例介绍-案例需求和数据介绍_.mp4 [41.8 MB]
08-【了解】数据处理-构建 字典_.mp4 [60.2 MB]
06-【了解】数据处理-构建语言对-思路分析_.mp4 [76.0 MB]
28-【补充】有关损失函数的2种用法_.mp4 [64.0 MB]
15-【了解】中午课程回顾_.mp4 [45.0 MB]
18-【了解】解码器-代码测试_.mp4 [35.2 MB]
07-【了解】数据处理-构建语言对-代码实现_.mp4 [57.3 MB]
25-【了解】模型训练-业务函数代码实现_.mp4 [76.3 MB]
09-【了解】数据处理-dataset和dataloader-思路分析_.mp4 [35.8 MB]
21-【重要】attention解码器-代码实现_.mp4 [56.9 MB]
20-【重要】attention解码器-思路分析_.mp4 [107.9 MB]
13-【重要】编码器-代码实现_.mp4 [54.9 MB]
16-【了解】解码器-思路分析_.mp4 [73.9 MB]
19-【答疑】解码时-省略了输入go出来y1_.mp4 [4.8 MB]
17-【了解】解码器-代码实现_.mp4 [31.8 MB]
14-【重要】编码器-代码测试_.mp4 [21.7 MB]
24-【了解】模型训练-业务函数思路分析_.mp4 [74.5 MB]
03-【了解】案例介绍-任务识别_.mp4 [34.9 MB]
05-【了解】数据处理-导包_.mp4 [60.9 MB]
11-【重要】数据处理-总结和练习_.mp4 [44.5 MB]
04-【了解】案例介绍-效果-小结和练习_.mp4 [30.1 MB]
12-【重要】编码器-思路分析_.mp4 [64.7 MB]
01-【重要】上一次课程复习_.mp4 [57.4 MB]
26-【重要】模型训练-内部训练函数-实现_.mp4 [93.0 MB]
22-【重要】attention解码器-代码调试_.mp4 [62.5 MB]
27-【重要】模型训练-模型训练小结_.mp4 [11.4 MB]
10-【了解】数据处理-dataset和dataloader代码实现_.mp4 [47.4 MB]
📁 📁 day03_RNN及其变体
13-【了解】数据处理-读数据到内存_.mp4 [37.2 MB]
01-【了解】文本数据分析-复习_.mp4 [46.4 MB]
30-【答疑】为什么一开始准确率很高-shuffle=False的原因_.mp4 [9.9 MB]
25-【了解】gru模型-构建实现_.mp4 [17.1 MB]
21-【重要】rnn模型-rnn模型的init_.mp4 [30.3 MB]
07-【答疑】链式求导-梯度消失-梯度爆炸-lstm缓解_.mp4 [32.0 MB]
28-【了解】rnn模型训练-代码调试_.mp4 [12.8 MB]
26-【重要】rnn数据形状练习_.mp4 [16.2 MB]
06-【了解】小结和练习_.mp4 [10.7 MB]
32-【了解】gru模型训练-实现_.mp4 [10.7 MB]
14-【了解】数据处理-三部曲解释_.mp4 [39.8 MB]
33-【了解】模型训练效果分析_.mp4 [38.2 MB]
24-【了解】lstm模型-构建实现_.mp4 [30.0 MB]
05-【重要】lstm-内部结构-pi函数_.mp4 [66.2 MB]
18-【了解】中午课程回顾_.mp4 [91.1 MB]
22-【重要】rnn模型-rnn模型的forward_.mp4 [32.3 MB]
19-【了解】数据处理-总结和练习_.mp4 [14.7 MB]
15-【重要】数据处理-构建dataset-dataloader-思路分析_.mp4 [40.1 MB]
20-【重要】rnn模型-思路分析_.mp4 [88.8 MB]
02-【重要】rnn-api复习_.mp4 [56.8 MB]
35-【重要】课堂答疑画图函数不能使用loss-item_.mp4 [22.8 MB]
23-【重要】rnn模型-rnn模型的测试给模型喂数据_.mp4 [24.1 MB]
29-【了解】rnn模型训练-代码实现_.mp4 [91.3 MB]
31-【了解】lstm模型训练-实现_.mp4 [16.7 MB]
34-【了解】小结和今天作业_.mp4 [15.3 MB]
12-【了解】数据处理-字母表-国家名_.mp4 [33.7 MB]
10-【课堂答疑】有关批量给RNN送数据是如何支持的_.mp4 [17.2 MB]
11-【重要】案例介绍_.mp4 [65.4 MB]
16-【重要】数据处理-构建dataset代码实现_.mp4 [36.7 MB]
17-【重要】数据处理-构建dataloader代码实现_.mp4 [22.9 MB]
03-【重要】lstm概念和内部结构_.mp4 [65.2 MB]
27-【了解】rnn模型训练-代码串讲_.mp4 [58.1 MB]
09-【了解】gru-api函数和小结_.mp4 [45.5 MB]
08-【了解】gru概念和内部结构_.mp4 [33.1 MB]
04-【问答】lstm为什么有记忆功能_.mp4 [19.6 MB]
📁 📁 day04_案例人名分类器
05-【了解】gru模型预测_.mp4 [8.1 MB]
09-【了解】实验课-修改日志的名字-操作梳理_.mp4 [19.4 MB]
13-【了解】实验课-把模型togpu是什么意思_.mp4 [29.6 MB]
08-【了解】实验课-转后台进程-实时查看后台进程日志_.mp4 [27.9 MB]
24-【答疑】每个时间步的3个动作_.mp4 [28.3 MB]
29-【重要】注意力机制公式-思路分析_.mp4 [108.1 MB]
16-【重要】注意力机制-概念和为什么_.mp4 [28.4 MB]
18-【重要】注意力机制-qkv栗子_.mp4 [23.1 MB]
22-【重要】seq2seq架构介绍_.mp4 [65.4 MB]
28-【强调】-最后梳理解码时每个时间步都有3个动作_.mp4 [7.7 MB]
04-【了解】lstm模型预测_.mp4 [8.0 MB]
06-【了解】小结和练习_.mp4 [10.3 MB]
27-【重要】小结和练习_.mp4 [17.4 MB]
15-【了解】实验课-有关loss是在gpu上还是cpu上-代码验证_.mp4 [20.6 MB]
33-【了解】小结和作业_.mp4 [8.0 MB]
11-【了解】实验课-有关GPU训练模型要点_.mp4 [56.9 MB]
21-【重要】中午课程复习_.mp4 [90.3 MB]
31-【了解】总结和练习_.mp4 [48.6 MB]
17-【重要】注意力机制-qkv概念_.mp4 [21.1 MB]
20-【了解】注意力机制小结和练习题_.mp4 [11.0 MB]
02-【了解】rnn模型预测-思路分析_.mp4 [40.7 MB]
01-【了解】上一次课程复习_.mp4 [81.6 MB]
07-【了解】实验课-服务器上训练模型-为什么要转后台程序_.mp4 [31.7 MB]
03-【了解】rnn模型预测-代码实现_.mp4 [48.5 MB]
25-【重要】注意力机制qkv运算的实际意义_.mp4 [31.8 MB]
12-【了解】实验课-模型todevice-数据todevice_.mp4 [83.9 MB]
30-【重要】注意力机制公式-代码实现_.mp4 [77.1 MB]
32-【重要】bmm运算矩阵运算-意义解读_.mp4 [31.4 MB]
23-【重要】seq2seq架构解码器中的qkv绍_.mp4 [55.1 MB]
10-【了解】实验课-经常的问题_.mp4 [43.4 MB]
19-【重要】注意力机制的2个步骤_.mp4 [59.4 MB]
14-【了解】实验课-有关loss是在gpu上还是cpu上_.mp4 [22.4 MB]
📁 📁 day09_迁移学习transformers
14-【重要】pipeline-文本分类代码实现_.mp4 [27.7 MB]
18-【了解】pipeline-阅读理解-摘要_.mp4 [43.9 MB]
17-【重要】pipeline-完形填空任务_.mp4 [18.2 MB]
06-【了解】自动超参数调优_.mp4 [19.9 MB]
27-【重要】automodel-mask任务-思路分析_.mp4 [57.8 MB]
15-【重要】pipeline-不带头特征抽取_.mp4 [30.1 MB]
25-【重要】automodel-提取特征-思路分析_.mp4 [84.7 MB]
12-【了解】hgface官网下载预训练模型_.mp4 [54.7 MB]
22-【重要】automodel-文本分类-编码_.mp4 [55.6 MB]
11-【了解】预训练模型分类_.mp4 [44.9 MB]
34-【重要】automodel-指定模型对比小结_.mp4 [22.3 MB]
05-【了解】fasttext调参-计算损失_.mp4 [13.6 MB]
16-【重要】pipeline-不带头特征抽取-代码实现_.mp4 [22.4 MB]
07-【了解】多标签多分类-损失函数更换_.mp4 [58.7 MB]
32-【重要】automodel小结_.mp4 [9.3 MB]
31-【了解】automodel-NER任务_.mp4 [58.4 MB]
20-【了解】中午课程回顾_.mp4 [31.9 MB]
04-【重要】学习率调整注意事情_.mp4 [19.1 MB]
01-【了解】fasttext复习_.mp4 [32.4 MB]
35-【了解】作业_.mp4 [6.3 MB]
21-【重要】automodel-文本分类_.mp4 [88.0 MB]
24-【重要】automodel-文本分类-注意点_.mp4 [21.9 MB]
33-【重要】指定模型方式-完型填空_.mp4 [46.3 MB]
02-【了解】fasttext调参-数据处理_.mp4 [73.6 MB]
26-【重要】automodel-提取特征-思路分析_.mp4 [33.9 MB]
13-【重要】pipeline-文本分类思路分析_.mp4 [33.1 MB]
10-【了解】迁移学习概念_.mp4 [57.7 MB]
29-【了解】automodel-抽取式问答_.mp4 [46.2 MB]
08-【了解】总结和练习_.mp4 [9.1 MB]
28-【重要】automodel-mask任务-代码实现_.mp4 [36.5 MB]
09-【了解】词向量迁移介绍_.mp4 [41.2 MB]
23-【重要】automodel-文本分类-注意点_.mp4 [32.9 MB]
19-【了解】pipeline-NER任务_.mp4 [20.6 MB]
03-【了解】fasttext调参-训练轮次-学习率-n-gram_.mp4 [28.3 MB]
📁 📁 day11_bert模型简介和总结
23-【重要】gpt工作处理过程_.mp4 [31.4 MB]
20-【重要】复习bert-源代码导读_.mp4 [83.3 MB]
01-【了解】上一次课程复习_.mp4 [91.2 MB]
08-【重要】bert模型-三大模型抽取事物特征-对比_.mp4 [47.6 MB]
11-【重要】bert模型预训练任务-mlm任务_.mp4 [62.3 MB]
24-【重要】gpt工作处理过程-小结_.mp4 [14.2 MB]
25-【了解】三大模型对比优缺点_.mp4 [8.8 MB]
10-【答疑】bert模型表征整个句子-101和102的区别_.mp4 [14.7 MB]
22-【了解】gpt工作方式简介_.mp4 [37.8 MB]
13-【了解】GLUE和CLUE_.mp4 [71.8 MB]
18-【了解】elmo二阶段训练_.mp4 [14.1 MB]
15-【重要】bert模型动态词向量支持实验_.mp4 [36.2 MB]
26-【了解】复习题_.mp4 [16.4 MB]
09-【重要】bert模型-Embedding-编码-微调方案_.mp4 [80.0 MB]
14-【重要】elmo模型支持动态词向量-抛转_.mp4 [60.4 MB]
05-【了解】nsp任务-模型评估_.mp4 [19.6 MB]
21-【重要】复习elmo_.mp4 [11.2 MB]
16-【重要】bert模型静态词向量支持实验_.mp4 [77.9 MB]
02-【了解】nsp任务-模型搭建_.mp4 [47.1 MB]
17-【重要】elmo的历史意义_.mp4 [14.7 MB]
19-【了解】elmo效果和改进点_.mp4 [14.4 MB]
06-【了解】nsp任务-小结和练习_.mp4 [15.5 MB]
12-【重要】bert模型预训练任务-nsp-小结和练习_.mp4 [25.1 MB]
07-【重要】bert模型简介和时间点_.mp4 [31.9 MB]
03-【了解】nsp任务-模型训练_.mp4 [34.2 MB]
04-【重要】答疑nsp任务关系是如何被bert模型表征的_.mp4 [19.2 MB]
📁 📁 day02_文本预处理下
04-【了解】句子长度分布-思路分析_.mp4 [32.3 MB]
20-【重要】rnnapi-主参数和辅助参数-实现_.mp4 [76.7 MB]
27-【重要】rnnapi-给模型喂数据的2种方式-实现_.mp4 [30.1 MB]
27-【重要】rnnapi-给模型喂数据的2种方式_.mp4 [43.5 MB]
01-【了解】课程复习_.mp4 [90.0 MB]
17-【重要】rnn模型结构_.mp4 [34.2 MB]
20-【重要】rnnapi-主参数和辅助参数-分析_.mp4 [37.2 MB]
15-【了解】中午课程复习_.mp4 [26.5 MB]
13-【了解】文本特征-文本长度规范_.mp4 [33.3 MB]
06-【了解】散点图-分析和实现_.mp4 [20.1 MB]
16-【了解】rnn模型的概念和作用_.mp4 [30.0 MB]
19-【重要】rnn内部结构_.mp4 [32.8 MB]
11-【了解】文本特征-n-gram特征_.mp4 [68.0 MB]
14-【了解】数据增强法和小结练习_.mp4 [23.4 MB]
23-【重要】rnnapi-参数研究.隐藏层1-2_.mp4 [45.2 MB]
09-【了解】词云生成-思路分析-代码调试_.mp4 [99.8 MB]
07-【了解】单词总数-思路分析_.mp4 [38.8 MB]
22-【重要】rnnapi-参数研究.batch-size_.mp4 [9.5 MB]
05-【了解】句子长度分布-代码实现_.mp4 [23.8 MB]
25-【答疑】rnn如何批量的处理数据_.mp4 [25.9 MB]
02-【了解】文本数据分析概念-语料介绍_.mp4 [55.1 MB]
28-【重要】总结和作业_.mp4 [38.1 MB]
26-【重要】rnnapi-参数研究.batchfirst_.mp4 [20.2 MB]
08-【了解】单词总数-代码实现_.mp4 [11.1 MB]
18-【了解】小结和练习_.mp4 [9.4 MB]
24-【重要】rnnapi-参数研究.隐藏层个数为n_.mp4 [31.6 MB]
10-【重要】文本数据分析总结和练习题_.mp4 [31.4 MB]
12-【了解】文本特征-zip函数_.mp4 [8.6 MB]
21-【重要】rnnapi-参数研究_.mp4 [39.2 MB]
03-【了解】标签数量分布-分析和实现_.mp4 [93.4 MB]
📁 源码课件笔记资料
📁 📁 阶段011-红蜘蛛知识图谱项目
📁 📁 day04
📁 📁 模型
📁 📁 bert_multi_head
📁 📁 .idea
misc.xml [185.0 B]
workspace.xml [12.4 KB]
multi_head_selection_code.iml [398.0 B]
modules.xml [302.0 B]
📁 📁 experiments
duie_selection_re.json [678.0 B]
📁 📁 metrics
📁 📁 __pycache__
__init__.cpython-37.pyc [175.0 B]
F1_score.cpython-36.pyc [3.1 KB]
__init__.cpython-36.pyc [205.0 B]
F1_score.cpython-37.pyc [3.2 KB]
F1_score.py [1.7 KB]
__init__.py [30.0 B]
📁 📁 bert-base-chinese
📁 📁 dataloaders
📁 📁 __pycache__
__init__.cpython-36.pyc [221.0 B]
__init__.cpython-37.pyc [191.0 B]
selection_loader.cpython-36.pyc [4.8 KB]
selection_loader.cpython-37.pyc [4.9 KB]
__init__.py [42.0 B]
selection_loader.py [5.2 KB]
📁 📁 config
📁 📁 __pycache__
hyper.cpython-37.pyc [344.0 B]
__init__.cpython-36.pyc [201.0 B]
hyper.cpython-36.pyc [401.0 B]
config.cpython-36.pyc [375.0 B]
__init__.cpython-37.pyc [170.0 B]
__init__.py [28.0 B]
config.py [150.0 B]
📁 📁 saved_model
📁 📁 models
📁 📁 __pycache__
__init__.cpython-36.pyc [204.0 B]
selection.cpython-37.pyc [7.7 KB]
selection.cpython-36.pyc [7.4 KB]
__init__.cpython-37.pyc [174.0 B]
selection.py [20.3 KB]
__init__.py [30.0 B]
📁 📁 preprocessings
📁 📁 __pycache__
duie_selection.cpython-37.pyc [5.8 KB]
duie_selection.cpython-36.pyc [5.6 KB]
__init__.cpython-36.pyc [225.0 B]
__init__.cpython-37.pyc [195.0 B]
duie_selection.py [8.0 KB]
__init__.py [43.0 B]
📁 📁 data
📁 📁 raw_data
📁 📁 duie
dev_data.json [27.1 MB]
train_data.json [215.6 MB]
all_50_schemas [3.9 KB]
📁 📁 duie
📁 📁 multi_head_selection
relation_vocab.json [793.0 B]
train_data.json [117.4 MB]
dev_data.json [14.8 MB]
bio_vocab.json [25.0 B]
word_vocab.json [86.0 KB]
predict.py [8.7 KB]
nohup.out [820.0 B]
main.py [6.6 KB]
下载 - 快捷方式.lnk.重命名 [688.0 B]
📁 📁 multi_head
📁 📁 dataloaders
📁 📁 __pycache__
selection_loader.cpython-36.pyc [4.5 KB]
__init__.cpython-36.pyc [216.0 B]
__init__.py [43.0 B]
selection_loader.py [6.3 KB]
📁 📁 models
📁 📁 __pycache__
selection.cpython-36.pyc [7.5 KB]
__init__.cpython-36.pyc [199.0 B]
selection.py [10.1 KB]
back_selection.py [10.1 KB]
__init__.py [30.0 B]
demo_selection.py [10.4 KB]
📁 📁 data
📁 📁 duie
📁 📁 multi_head_selection
bio_vocab.json [36.0 B]
relation_vocab.json [793.0 B]
word_vocab.json [86.0 KB]
train_data.json [102.6 MB]
dev_data.json [12.8 MB]
📁 📁 preprocessings
📁 📁 __pycache__
duie_selection.cpython-36.pyc [5.6 KB]
__init__.cpython-36.pyc [220.0 B]
__init__.py [43.0 B]
duie_selection.py [5.1 KB]
📁 📁 .idea
modules.xml [292.0 B]
misc.xml [185.0 B]
multi_head_selection.iml [398.0 B]
workspace.xml [23.6 KB]
📁 📁 experiments
duie_selection_re.json [637.0 B]
📁 📁 saved_models
📁 📁 config
📁 📁 __pycache__
hyper.cpython-36.pyc [369.0 B]
__init__.cpython-36.pyc [195.0 B]
__init__.py [27.0 B]
hyper.py [150.0 B]
📁 📁 metrics
📁 📁 __pycache__
__init__.cpython-36.pyc [200.0 B]
F1_score.cpython-36.pyc [3.1 KB]
F1_score.py [1.7 KB]
__init__.py [30.0 B]
predict.py [8.3 KB]
main.py [5.2 KB]
📁 📁 day03
📁 📁 IDCNN_BERT
📁 📁 model
📁 📁 __pycache__
__init__.cpython-37.pyc [229.0 B]
crf.cpython-37.pyc [6.1 KB]
bert_lstm_crf.cpython-37.pyc [2.5 KB]
bert_lstm_crf.cpython-36.pyc [2.5 KB]
cnn.cpython-36.pyc [3.5 KB]
cnn.cpython-37.pyc [3.4 KB]
crf.cpython-36.pyc [6.2 KB]
__init__.cpython-36.pyc [253.0 B]
cnn.py [3.1 KB]
bert_lstm_crf.py [3.1 KB]
__init__.py [87.0 B]
crf.py [9.2 KB]
📁 📁 saved_model
back_bert_idcnn_lstm_crf.pt [392.3 MB]
📁 📁 data
📁 📁 bert
config.json [520.0 B]
vocab.txt [107.0 KB]
pytorch_model.bin [392.5 MB]
test.txt [170.1 KB]
train.txt [543.5 KB]
📁 📁 __pycache__
utils.cpython-36.pyc [5.2 KB]
constants.cpython-36.pyc [912.0 B]
constants.cpython-37.pyc [1.0 KB]
config.cpython-36.pyc [821.0 B]
utils.cpython-37.pyc [5.2 KB]
utils.py [6.5 KB]
Wrapper.py [2.7 KB]
config.py [652.0 B]
README.md [2.6 KB]
train.py [3.3 KB]
📁 📁 预训练模型
📁 📁 xlnet
spiece.model [675.2 KB]
special_tokens_map.json [203.0 B]
tokenizer.json [1.2 MB]
config.json [672.0 B]
added_tokens.json [3.0 B]
pytorch_model.bin [445.4 MB]
tokenizer_config.json [20.0 B]
📁 📁 T5
special_tokens_map.json [112.0 B]
test_generations.txt [2.0 KB]
test_results.json [395.0 B]
trainer_state.json [56.4 KB]
config.json [676.0 B]
pytorch_model.bin [818.5 MB]
train_results.json [464.0 B]
val_results.json [406.0 B]
vocab.txt [107.7 KB]
tokenizer_config.json [426.0 B]
training_args.bin [2.5 KB]
all_results.json [1.2 KB]
📁 📁 electra_base_discriminator
special_tokens_map.json [112.0 B]
added_tokens.json [2.0 B]
tokenizer_config.json [19.0 B]
pytorch_model.bin [390.2 MB]
vocab.txt [107.0 KB]
tokenizer.json [262.7 KB]
config.json [441.0 B]
day03课堂问题.md [817.0 B]
📁 📁 实训1
📁 📁 3组
📁 📁 1组
📁 📁 idcnn_gai
📁 📁 saved_model
📁 📁 .idea
📁 📁 inspectionProfiles
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profiles_settings.xml [174.0 B]
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workspace.xml [4.1 KB]
idcnn_gai.iml [452.0 B]
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modules.xml [277.0 B]
📁 📁 __pycache__
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config.cpython-36.pyc [813.0 B]
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config.cpython-38.pyc [764.0 B]
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📁 📁 model
📁 📁 __pycache__
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cnn.cpython-37.pyc [3.4 KB]
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cnn.cpython-311.pyc [5.9 KB]
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idcnn_crf.cpython-38.pyc [2.0 KB]
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cnn.cpython-38.pyc [3.4 KB]
crf.cpython-38.pyc [4.9 KB]
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📁 📁 .idea
📁 📁 inspectionProfiles
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cnn.py [4.0 KB]
crf.py [14.3 KB]
📁 📁 data
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output.txt [48.0 B]
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config.py [764.0 B]
README.md [505.0 B]
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📁 📁 4组
📁 📁 2组
📁 📁 idcnn_macbert
📁 📁 data
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vocab.txt [107.0 KB]
process.py [767.0 B]
test.txt [170.1 KB]
train.txt [543.5 KB]
output.txt [48.0 B]
📁 📁 model
📁 📁 __pycache__
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cnn.py [4.0 KB]
idcnn_crf.py [3.2 KB]
crf.py [14.3 KB]
📁 📁 __pycache__
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config.cpython-38.pyc [774.0 B]
📁 📁 saved_model
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utils.py [6.6 KB]
inference.py [2.7 KB]
config.py [771.0 B]
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📁 📁 5组
📁 📁 idcnn
📁 📁 model
📁 📁 __pycache__
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crf.cpython-37.pyc [4.9 KB]
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idcnn_crf.py [3.0 KB]
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cnn1.py [2.3 KB]
bert.py [2.7 KB]
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cnn.py [4.1 KB]
📁 📁 saved_model
📁 📁 __pycache__
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config.cpython-37.pyc [820.0 B]
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input.txt [41.0 B]
test.txt [170.1 KB]
vocab.txt [107.0 KB]
train.txt [543.5 KB]
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📁 📁 6组
📁 📁 idcnn_
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config.cpython-311.pyc [927.0 B]
📁 📁 data
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vocab.txt [107.0 KB]
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input.txt [41.0 B]
📁 📁 .idea
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📁 📁 model
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📁 📁 saved_model
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📁 📁 Bert-IDCNN-CRF
📁 📁 model
📁 📁 __pycache__
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📁 📁 __pycache__
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config.py [764.0 B]
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📁 📁 day01
📁 📁 红蜘蛛讲义_课堂版
📁 📁 site
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📁 📁 javascripts
📁 📁 lunr
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📁 📁 javascripts
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12_1.md [6.7 KB]
10_3.md [7.4 KB]
1_3.md [8.1 KB]
1_2.md [3.1 KB]
12_2.md [14.9 KB]
2_2.md [5.4 KB]
9_1.md [44.3 KB]
7_2.md [6.2 KB]
15_1.md [8.7 KB]
6_1.md [13.0 KB]
3_2.md [3.5 KB]
8_2.md [2.5 KB]
4_3.md [3.0 KB]
1_1.md [2.1 KB]
8_1.md [11.2 KB]
4_4.md [310.0 B]
13_1.md [55.9 KB]
index.md [181.0 B]
14_1.md [35.2 KB]
15_3.md [5.8 KB]
6_3.md [17.3 KB]
2_1.md [2.8 KB]
3_4.md [3.2 KB]
11_2.md [31.1 KB]
15_2.md [3.7 KB]
9_2.md [4.7 KB]
3_1.md [32.7 KB]
10_1.md [7.6 KB]
3_3.md [4.2 KB]
11_1.md [15.8 KB]
mkdocs.yml [2.6 KB]
📁 📁 预训练模型
📁 📁 ernie3.0-base-chinese
vocab.txt [182.4 KB]
pytorch_model.bin [452.4 MB]
config.json [534.0 B]
📁 📁 T5
val_results.json [406.0 B]
pytorch_model.bin [818.5 MB]
config.json [676.0 B]
tokenizer_config.json [426.0 B]
all_results.json [1.2 KB]
trainer_state.json [56.4 KB]
training_args.bin [2.5 KB]
vocab.txt [107.7 KB]
test_generations.txt [2.0 KB]
special_tokens_map.json [112.0 B]
train_results.json [464.0 B]
test_results.json [395.0 B]
📁 📁 roberta_chinese_wwm_ext
pytorch_model.bin [392.5 MB]
config.json [689.0 B]
special_tokens_map.json [112.0 B]
added_tokens.json [2.0 B]
tokenizer_config.json [19.0 B]
vocab.txt [107.0 KB]
tokenizer.json [262.7 KB]
📁 📁 albert_chinese_base
pytorch_model.bin [40.7 MB]
vocab.txt [107.0 KB]
config.json [653.0 B]
📁 📁 macbert_chinese_base
added_tokens.txt [2.0 B]
pytorch_model.bin [392.5 MB]
special_tokens_map.json [112.0 B]
tokenizer.json [262.7 KB]
config.json [659.0 B]
tokenizer_config.json [19.0 B]
vocab.txt [107.0 KB]
📁 📁 idcnn
📁 📁 data
input.txt [41.0 B]
train.txt [543.5 KB]
process.py [767.0 B]
vocab.txt [107.0 KB]
test.txt [170.1 KB]
output.txt [48.0 B]
📁 📁 model
📁 📁 __pycache__
cnn.cpython-37.pyc [3.4 KB]
crf.cpython-36.pyc [4.9 KB]
__init__.cpython-36.pyc [258.0 B]
__init__.cpython-37.pyc [229.0 B]
crf.cpython-37.pyc [6.1 KB]
bert_lstm_crf.cpython-37.pyc [2.5 KB]
idcnn_crf.cpython-36.pyc [1.9 KB]
bert_lstm_crf.cpython-36.pyc [2.5 KB]
cnn.cpython-36.pyc [3.5 KB]
cnn.py [4.0 KB]
crf.py [14.3 KB]
__init__.py [79.0 B]
idcnn_crf.py [3.0 KB]
📁 📁 __pycache__
utils.cpython-37.pyc [5.2 KB]
config.cpython-36.pyc [813.0 B]
utils.cpython-36.pyc [5.2 KB]
constants.cpython-37.pyc [1.0 KB]
constants.cpython-36.pyc [907.0 B]
📁 📁 saved_model
idcnn_crf.pt [24.4 MB]
inference.py [2.7 KB]
config.py [764.0 B]
utils.py [6.7 KB]
train.py [4.1 KB]
README.md [505.0 B]
day01课堂问题.md [4.2 KB]
📁 📁 day07
📁 📁 data
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
Project_Default.xml [371.0 B]
misc.xml [188.0 B]
workspace.xml [1.6 KB]
.gitignore [190.0 B]
modules.xml [267.0 B]
data.iml [291.0 B]
train.txt [47.6 KB]
dev.txt [47.6 KB]
stopwords.txt [5.1 KB]
class.txt [26.0 B]
demo.py [370.0 B]
test.txt [47.6 KB]
📁 📁 red_spider4
📁 📁 __pycache__
question_parser.cpython-37.pyc [2.0 KB]
question_parser.cpython-36.pyc [2.0 KB]
question_classifier.cpython-38.pyc [4.7 KB]
config.cpython-38.pyc [258.0 B]
onnx_question_classifier.cpython-36.pyc [5.8 KB]
question_parser.cpython-38.pyc [2.2 KB]
onnx_question_classifier.cpython-38.pyc [5.5 KB]
answer_search.cpython-38.pyc [2.6 KB]
answer_search.cpython-36.pyc [1.9 KB]
config.cpython-36.pyc [258.0 B]
answer_search.cpython-37.pyc [2.0 KB]
question_classifier.cpython-36.pyc [5.0 KB]
question_classifier.cpython-37.pyc [4.1 KB]
config.cpython-37.pyc [226.0 B]
📁 📁 dict
drug.txt [72.9 KB]
department.txt [593.0 B]
symptom.txt [97.2 KB]
food.txt [73.3 KB]
check.txt [70.0 KB]
disease.txt [173.4 KB]
deny.txt [265.0 B]
producer.txt [495.8 KB]
📁 📁 data
model.onnx [390.4 MB]
medical.json [45.0 MB]
📁 📁 models
📁 📁 __pycache__
bert.cpython-36.pyc [2.3 KB]
bert.cpython-38.pyc [2.3 KB]
textCNN.cpython-36.pyc [3.0 KB]
bert.py [2.3 KB]
onnx_question_classifier.py [7.8 KB]
answer_search.py [3.0 KB]
demo_onnx.py [8.4 KB]
config.py [114.0 B]
demo_classifier.py [6.8 KB]
chatbot.py [1.4 KB]
build_medicalgraph.py [4.9 KB]
question_parser.py [2.7 KB]
question_classifier.py [6.7 KB]
question_classifier.py [6.7 KB]
📁 📁 day05
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
Project_Default.xml [273.0 B]
.gitignore [184.0 B]
deployment.xml [1.7 KB]
workspace.xml [4.0 KB]
misc.xml [326.0 B]
day05.iml [291.0 B]
modules.xml [269.0 B]
📁 📁 back_red
📁 📁 dict
drug.txt [72.9 KB]
disease.txt [173.4 KB]
symptom.txt [97.2 KB]
deny.txt [265.0 B]
producer.txt [495.8 KB]
department.txt [593.0 B]
check.txt [70.0 KB]
food.txt [73.3 KB]
📁 📁 __pycache__
question_classifier.cpython-38.pyc [4.1 KB]
question_parser.cpython-38.pyc [2.0 KB]
question_parser.cpython-37.pyc [2.0 KB]
question_classifier.cpython-37.pyc [4.1 KB]
config.cpython-310.pyc [229.0 B]
answer_search.cpython-37.pyc [2.0 KB]
config.cpython-311.pyc [251.0 B]
config.cpython-37.pyc [215.0 B]
answer_search.cpython-38.pyc [2.0 KB]
config.cpython-38.pyc [246.0 B]
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
.gitignore [184.0 B]
.name [21.0 B]
deployment.xml [431.0 B]
back_red.iml [324.0 B]
misc.xml [188.0 B]
workspace.xml [6.1 KB]
modules.xml [275.0 B]
📁 📁 data
medical.json [45.0 MB]
temp.json [647.0 KB]
back_medical.json [45.0 MB]
📁 📁 dict - 副本
deny.txt [265.0 B]
check.txt [70.0 KB]
drug.txt [72.9 KB]
symptom.txt [97.2 KB]
producer.txt [495.8 KB]
disease.txt [173.4 KB]
food.txt [73.3 KB]
department.txt [593.0 B]
question_parser.py [2.2 KB]
answer_search.py [2.1 KB]
build_medicalgraph.py [5.0 KB]
question_classifier.py [5.0 KB]
config.py [113.0 B]
chatbot.py [1.6 KB]
📁 📁 day06
📁 📁 gpt2_chinese_base
vocab.txt [107.0 KB]
config.json [605.0 B]
pytorch_model.bin [401.4 MB]
📁 📁 red_spider1
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
Project_Default.xml [273.0 B]
.gitignore [184.0 B]
workspace.xml [3.1 KB]
misc.xml [189.0 B]
red_spider1.iml [495.0 B]
deployment.xml [1.7 KB]
modules.xml [281.0 B]
📁 📁 data
medical.json [45.0 MB]
📁 📁 dict
check.txt [70.0 KB]
food.txt [73.3 KB]
symptom.txt [97.2 KB]
disease.txt [173.4 KB]
department.txt [594.0 B]
producer.txt [495.8 KB]
drug.txt [72.9 KB]
deny.txt [265.0 B]
📁 📁 __pycache__
question_parser.cpython-37.pyc [7.1 KB]
app.cpython-37.pyc [1.5 KB]
chat_gpt.cpython-39.pyc [1023.0 B]
question_classifier.cpython-37.pyc [8.5 KB]
yuyuan.cpython-39.pyc [972.0 B]
answer_search.cpython-37.pyc [8.1 KB]
config.cpython-39.pyc [227.0 B]
question_classifier.cpython-39.pyc [8.3 KB]
config.cpython-37.pyc [249.0 B]
gpt2.cpython-39.pyc [1.0 KB]
qwen.cpython-39.pyc [1004.0 B]
question_classifier.cpython-38.pyc [8.5 KB]
gpt2.py [766.0 B]
question_parser.py [8.5 KB]
answer_search.py [9.3 KB]
chat_gpt.py [1.1 KB]
build_medicalgraph.py [13.5 KB]
test.py [536.0 B]
config.py [369.0 B]
qwen.py [883.0 B]
chatbot.py [2.8 KB]
yuyuan.py [675.0 B]
question_classifier.py [11.6 KB]
app.py [2.0 KB]
📁 📁 Yuyuan
special_tokens_map.json [90.0 B]
pytorch_model.bin [6.7 GB]
generation_example.jpg [198.1 KB]
README.md [1.7 KB]
tokenizer_config.json [236.0 B]
config.json [785.0 B]
merges.txt [445.7 KB]
tokenizer.json [1.3 MB]
vocab.json [779.5 KB]
📁 📁 Qwen-7b
📁 📁 Qwen-7B-Chat
pytorch_model-00004-of-00008.bin [1.9 GB]
pytorch_model-00006-of-00008.bin [1.9 GB]
pytorch_model-00007-of-00008.bin [1.9 GB]
pytorch_model-00002-of-00008.bin [1.9 GB]
pytorch_model-00001-of-00008.bin [1.8 GB]
pytorch_model-00008-of-00008.bin [1.2 GB]
pytorch_model.bin.index.json [19.1 KB]
pytorch_model-00005-of-00008.bin [1.9 GB]
pytorch_model-00003-of-00008.bin [1.9 GB]
NOTICE.txt [2.6 KB]
qwen.tiktoken [2.4 MB]
config.json [1.1 KB]
README.md [19.5 KB]
generation_config.json [194.0 B]
qwen_generation_utils.py [14.3 KB]
modeling_qwen.py [44.3 KB]
pytorch_model.bin.index.json [19.1 KB]
LICENSE.txt [6.7 KB]
configuration_qwen.py [2.4 KB]
gitattributes.txt [1.5 KB]
tokenizer_config.json [173.0 B]
tokenization_qwen.py [7.8 KB]
build_medicalgraph.py [13.5 KB]
📁 📁 day02
day02课堂问题.md [1.4 KB]
📁 📁 day08
📁 📁 面试题
NLP基础模拟面试10道题.md [8.7 KB]
评价表模板_3.pdf [303.7 KB]
评价表模板.pdf [143.1 KB]
姚老师组面试题无答案.md [2.4 KB]
NLP基础模拟面试题.pdf [522.1 KB]
模拟面试题.md [4.0 KB]
评价表模板_2(1).pdf [373.9 KB]
day08课堂笔记.txt [18.5 KB]
姚老师组面试题.pdf [706.6 KB]
day08课堂笔记.txt [18.5 KB]
📁 📁 阶段012-CHAT_GPT与大模型
📁 📁 bert-base-chinese
flax_model.msgpack [390.2 MB]
tokenizer_config.json [29.0 B]
README.md [21.0 B]
pytorch_model.bin [392.5 MB]
vocab.txt [107.0 KB]
tokenizer.json [262.6 KB]
.gitattributes [391.0 B]
config.json [624.0 B]
📁 📁 day03
📁 📁 03-代码
📁 📁 03-代码_20240401_195154
📁 📁 01-课件
📁 📁 第三章：大模型微调主要方式
01-大模型Prompt-Tuning技术入门.pdf [2.4 MB]
02-大模型Prompt-Tuning技术进阶.pdf [1.6 MB]
03-大模型应用框架-LangChain.pdf [1.1 MB]
📁 📁 day04
📁 📁 01-课件
📁 📁 第三章：大模型微调主要方式
03-大模型应用框架-LangChain.pdf [1.1 MB]
02-大模型Prompt-Tuning技术进阶.pdf [1.6 MB]
01-大模型Prompt-Tuning技术入门.pdf [2.4 MB]
📁 📁 03-代码
📁 📁 langchain_use
📁 📁 langchain_glm
model.py [1.4 KB]
__init__.py
📁 📁 Indexes_module
js_apply.py [1.1 KB]
AI17_retriever.py [804.0 B]
ts_apply.py [399.0 B]
AI17_dc.py [499.0 B]
pku1.txt [1.7 KB]
vc_apply.py [758.0 B]
AI17_ds.py [412.0 B]
衣服属性.txt [819.0 B]
dc_apply.py [428.0 B]
AI17_Vector.py [873.0 B]
pku.txt [1.7 KB]
📁 📁 Prompts_module
few-shot.py [1.1 KB]
AI17_few_shot_prompt.py [960.0 B]
zero-shot.py [606.0 B]
__init__.py
AI17_zero_shot_prompt.py [705.0 B]
📁 📁 Agents_module
AI17_agent.py [953.0 B]
agents_apply.py [968.0 B]
__init__.py
Ai17_findall_tools.py [93.0 B]
📁 📁 Knowledge_QA
📁 📁 moka-ai
📁 📁 m3e-base
📁 📁 1_Pooling
config.json [190.0 B]
model.safetensors [390.1 MB]
special_tokens_map.json [125.0 B]
tokenizer.json [428.8 KB]
modules.json [229.0 B]
vocab.txt [107.0 KB]
sentence_bert_config.json [53.0 B]
tokenizer_config.json [342.0 B]
config.json [932.0 B]
gitattributes [1.5 KB]
pytorch_model.bin [390.2 MB]
README.md [26.0 KB]
📁 📁 faiss
📁 📁 product
index.faiss [15.0 KB]
index.pkl [1.6 KB]
test.py [822.0 B]
model.py [1.4 KB]
main.py [1.4 KB]
__init__.py
衣服属性.txt [819.0 B]
get_vector.py [1.2 KB]
📁 📁 Chain_module
__init__.py
AI17-zero-shot_chain.py [561.0 B]
AI7_muti_shot_chain.py [1.0 KB]
zero-shot-langchain.py [562.0 B]
Simple_Sequential_Chain.py [1.0 KB]
📁 📁 Models_module
📁 📁 google
📁 📁 flan-t5-small
gitattributes [1.4 KB]
tokenizer.json [2.3 MB]
config.json [1.4 KB]
README.md [10.6 KB]
spiece.model [773.1 KB]
tokenizer_config.json [2.5 KB]
generation_config.json [147.0 B]
flax_model.msgpack [293.6 MB]
special_tokens_map.json [2.1 KB]
pytorch_model.bin [293.6 MB]
model.safetensors [293.6 MB]
llms_apply.py [280.0 B]
test.py [813.0 B]
AI17_ChatModel.py [777.0 B]
chatModel_prompt.py [824.0 B]
embeddingModel.py [402.0 B]
AI17_ChatPrompt.py [1.0 KB]
AI17_embedding.py [652.0 B]
__init__.py
chatModel_apply.py [1.9 KB]
📁 📁 Memory_module
MH_llm_apply.py [597.0 B]
__init__.py
AI17_message_dict.py [381.0 B]
AI17_Message_history.py [652.0 B]
test.py [975.0 B]
MessageHistory_apply.py [183.0 B]
AI17_test.py [296.0 B]
📁 📁 05-预习代码
📁 📁 PET
📁 📁 utils
common_utils.py [4.6 KB]
metirc_utils.py [4.4 KB]
__init__.py
verbalizer.py [7.9 KB]
📁 📁 Documents
📁 📁 NetSarang Computer
📁 📁 7
📁 📁 Themes
📁 📁 SECSH
📁 📁 HostKeys
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📁 📁 Common
MasterPassword.mpw [116.0 B]
📁 📁 Xshell
📁 📁 HighlightSet Files
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📁 📁 applog
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📁 📁 Sessions
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📁 📁 QuickButton Files
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📁 📁 Scripts
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📁 📁 Logs
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📁 📁 ColorScheme Files
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Xshell.ini [1.0 KB]
buttonlist.ini [48.0 B]
CustomKeyMap.ckm [4.2 KB]
📁 📁 Xftp
📁 📁 Sessions
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📁 📁 Logs
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📁 📁 Temporary
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📁 📁 applog
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LocalBookmark.ini [44.0 B]
buttonlist.ini [48.0 B]
Xftp.ini [32.0 B]
📁 📁 Adobe
📁 📁 After Effects 2023
📁 📁 Video Libraries
📁 📁 User Presets
(Adobe) [917.0 B]
📁 📁 User Libraries
📁 📁 Common
📁 📁 PTX
2048e22e-8781-8d52-269c-c15800009ff0.ocl [25.6 KB]
c0f5ac31-8148-854c-7170-e0b1000027eb.ocl [20.5 KB]
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ab6a0700-811a-6960-9491-89360000c04a.ocl [22.0 KB]
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f0c97698-5709-08c4-c43a-126100006a77.ocl [97.7 KB]
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5b470a7f-4c9a-2eb5-62d8-7d1e0002360a.ocl [374.3 KB]
2d60a6b7-60ea-0b92-63bb-e89600021698.ocl [1.2 MB]
7e407581-9fa0-08d2-cd69-1bf700026e4f.ocl [1.1 MB]
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daeadabc-01f4-c762-7e99-92660000c141.ocl [78.3 KB]
fbcab6f3-8b16-bf63-83c4-e2ac0000cfe2.ocl [57.2 KB]
56da206a-b87b-8b69-05dd-9efa0000b20c.ocl [46.9 KB]
b69235f5-064e-0386-22eb-372a0000a4d5.ocl [25.7 KB]
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29c2818a-2039-f6ad-56ed-22910001aa0a.ocl [188.1 KB]
c8e4c8bc-1eda-a69a-1a3e-4fab0000baa1.ocl [56.0 KB]
d9d3e30c-7b56-e2c9-febd-40da00009cd6.ocl [31.9 KB]
498f6448-fd4b-5317-d567-1c5f0000309a.ocl [24.4 KB]
3adf4424-c37c-77b2-b41b-8b180001c58f.ocl [619.9 KB]
8ee673dd-6431-ddf9-b7a0-ae190001bc8b.ocl [260.0 KB]
📁 📁 Premiere Pro
📁 📁 23.0
📁 📁 Profile-王建兴
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AMERequestDB [2.0 KB]
Plugin Loading.log [353.1 KB]
Extension Config.xml [218.0 B]
📁 📁 NewBlue
📁 📁 Titler Pro
📁 📁 Library
📁 📁 Effects
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Default.nbtitle [18.0 KB]
📁 📁 WeChat Files
📁 📁 wxid_tn62452emllm22
📁 📁 FileStorage
📁 📁 Temp
…（已达最大深度 10 层，子目录未展开）
📁 📁 Navicat
📁 📁 MySQL
📁 📁 profiles
vgroup.xml [61.0 B]
📁 📁 servers
📁 📁 localhost
…（已达最大深度 10 层，子目录未展开）
📁 📁 logs
LogHistory.txt [6.4 KB]
📁 📁 Digital Anarchy
Licenses.txt [385.0 B]
10.22作业.md [4.5 KB]
更换为模型派.png [207.8 KB]
Day02KNN.md [406.0 B]
12.1面试10道题.md [6.5 KB]
📁 📁 data
prompt.txt [37.0 B]
dev.txt [98.7 KB]
verbalizer.txt [139.0 B]
train.txt [9.6 KB]
📁 📁 checkpoints
📁 📁 model_400
tokenizer_config.json [372.0 B]
tokenizer.json [428.8 KB]
config.json [870.0 B]
vocab.txt [107.0 KB]
special_tokens_map.json [125.0 B]
pytorch_model.bin [390.3 MB]
📁 📁 model_best
generation_config.json [90.0 B]
model.safetensors [390.2 MB]
vocab.txt [107.0 KB]
tokenizer_config.json [1.2 KB]
pytorch_model.bin [390.3 MB]
tokenizer.json [428.8 KB]
config.json [866.0 B]
special_tokens_map.json [125.0 B]
📁 📁 data_handle
test.py [774.0 B]
data_preprocess.py [4.6 KB]
__init__.py
data_loader.py [1.8 KB]
template.py [5.0 KB]
__init__.py
train.py [7.0 KB]
下载.lnk [700.0 B]
inference.py [3.8 KB]
pet_config.py [1.1 KB]
nohup.out [8.6 KB]
📁 📁 04-预习课件
📁 📁 第五章：基于Prompt方法的小样本文本分类实战
05-BERT+P-Tuning方式数据处理介绍.pdf [500.4 KB]
03-BERT+PET方式模型代码实现与训练.pdf [637.9 KB]
01-BERT+PET方式文本分类介绍.pdf [964.1 KB]
02-BERT+PET方式数据处理介绍.pdf [526.9 KB]
06-BERT+P-Tuning方式模型代码实现与训练.pdf [671.4 KB]
04-BERT+P-Tuning方式文本分类介绍.pdf [937.8 KB]
📁 📁 day01
📁 📁 03-代码
📁 📁 LLM_Base-day01
ROUGE_demo.py [655.0 B]
BLEU_demo.py [1.1 KB]
PPL_demo.py [890.0 B]
__init__.py
📁 📁 01-课件
📁 📁 第一章：大模型背景简介
01-LLM基础知识.pdf [1.7 MB]
02-LLM主要类别架构.pdf [2.8 MB]
📁 📁 04-预习资料
📁 📁 第二章：主流大模型介绍
02-LLM主流开源代表模型.pdf [1.1 MB]
01-ChatGPT模型原理.pdf [5.1 MB]
📁 📁 day02
📁 📁 03-预习资料
📁 📁 第三章：大模型微调主要方式
02-大模型Prompt-Tuning技术进阶.pdf [1.6 MB]
01-大模型Prompt-Tuning技术入门.pdf [2.4 MB]
03-大模型应用框架-LangChain.pdf [1.1 MB]
📁 📁 01-课件
📁 📁 第二章：主流大模型介绍
02-LLM主流开源代表模型.pdf [1.1 MB]
01-ChatGPT模型原理.pdf [5.1 MB]
📁 📁 阶段9-算法初识
📁 📁 代码
📁 📁 _04_LinkedList
📁 📁 __pycache__
LinkedList.cpython-311.pyc [3.6 KB]
Node.cpython-311.pyc [728.0 B]
Node.py [151.0 B]
test.py [4.7 KB]
LinkedList.py [2.4 KB]
📁 📁 _07_dp
fibnacci2.py [775.0 B]
dptest.py [2.0 KB]
fibnacci.py [289.0 B]
📁 📁 _05_stack&queue
Stack.py [1.7 KB]
Mystack2.py [843.0 B]
MovingAverage.py [554.0 B]
Mystack.py [913.0 B]
MovingAverage2.py [826.0 B]
MyQueue.py [685.0 B]
QueueTest.py [935.0 B]
📁 📁 _06_BinaryTree
TreeNode.py [4.6 KB]
backtracking.py [1.8 KB]
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
Project_Default.xml [431.0 B]
workspace.xml [9.5 KB]
modules.xml [271.0 B]
deployment.xml [1.0 KB]
misc.xml [210.0 B]
.gitignore [184.0 B]
代码.iml [291.0 B]
leetcode_string.py [3.5 KB]
leetcode_search.py [4.1 KB]
leetcode_array.py [3.2 KB]
📁 📁 笔记
📁 📁 assets
image-20231119181049094.png [88.2 KB]
image-20231119164224017.png [58.5 KB]
image-20231119170008787.png [12.2 KB]
笔记.md [12.7 KB]
📁 📁 课件
10_动态规划和贪心.pptx [368.8 KB]
03_基础算法之排序.pptx [5.5 MB]
02_算法复杂度介绍.pptx [377.5 KB]
05_字符串相关问题.pptx [501.8 KB]
01_算法面试介绍.pptx [48.9 MB]
04_数组相关问题 .pptx [591.5 KB]
09_递归与回溯.pptx [1.2 MB]
08_栈_队列相关问题.pptx [893.8 KB]
07_链表相关问题.pptx [749.1 KB]
06_查找相关问题.pptx [13.1 MB]
📁 📁 画图
字符列表.png [61.2 KB]
📁 📁 阶段6-深度学习基础
📁 📁 05.作业
02-神经网络.txt [433.0 B]
04-RNN.txt [125.0 B]
03-CNN.txt [128.0 B]
01-pytorch框架.txt [202.0 B]
📁 📁 01.讲义
03-卷积神经网络.pptx [4.0 MB]
GPU开发环境.pdf [1.7 MB]
00-深度学习简介.pptx [1.7 MB]
04-循环神经网络.pptx [1.8 MB]
02-神经网络基础.pptx [4.5 MB]
01-PyTorch基本使用.pptx [2.0 MB]
📁 📁 02.code
📁 📁 03-CNN
📁 📁 .idea
📁 📁 inspectionProfiles
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📁 📁 data
📁 📁 cifar-10-batches-py
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image_classification.pth [320.4 KB]
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📁 📁 01-pytorch的应用
📁 📁 .idea
📁 📁 inspectionProfiles
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📁 📁 02-神经网络
📁 📁 .idea
📁 📁 inspectionProfiles
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📁 📁 data
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phone.pth [145.8 KB]
phone2.pth [15.7 KB]
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06-EMP.py [466.0 B]
10-案例.py [2.9 KB]
08-lr.py [719.0 B]
📁 📁 04-RNN
📁 📁 data
jaychou_lyrics.txt [167.2 KB]
lyrics_model_2.pth [8.8 MB]
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
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modules.xml [264.0 B]
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📁 📁 深度学习.mindnode
📁 📁 QuickLook
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📁 📁 style.mindnodestyle
contents.xml [6.4 KB]
metadata.plist [391.0 B]
📁 📁 resources
contents.xml [90.1 KB]
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📁 📁 03.笔记
📁 📁 images
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01-pytorch框架.md [5.4 KB]
03-CNN.md [837.0 B]
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📁 📁 课前说明.mindnode
📁 📁 resources
📁 📁 QuickLook
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📁 📁 style.mindnodestyle
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📁 📁 阶段010-投满分项目V4
📁 📁 05-code_edit
📁 📁 04-distill
📁 📁 src
📁 📁 models
📁 📁 __pycache__
textCNN.cpython-37.pyc [1.6 KB]
textCNN.py [2.4 KB]
bert.py [2.3 KB]
📁 📁 save_dict
📁 📁 __pycache__
utils.cpython-37.pyc [2.6 KB]
utils.py [8.3 KB]
train_eval.py [10.1 KB]
run.py [755.0 B]
📁 📁 data
📁 📁 data
vocab.pkl [73.3 KB]
class.txt [82.0 B]
dev.txt [538.4 KB]
test.txt [538.7 KB]
📁 📁 bert_pretrain
vocab.txt [107.0 KB]
bert_config.json [520.0 B]
pytorch_model.bin [392.5 MB]
📁 📁 01-randomForest
📁 📁 data
dev.txt [538.4 KB]
dev_new.csv [1.1 MB]
class.txt [83.0 B]
stopwords.txt [5.1 KB]
test.txt [538.7 KB]
rf.py [1003.0 B]
ana.py [1.0 KB]
📁 📁 02-fasttext
📁 📁 data
dev_fast.txt [864.9 KB]
class.txt [83.0 B]
preprocess.py [937.0 B]
dev.txt [538.4 KB]
test.txt [538.7 KB]
preprocess1.py [962.0 B]
fastext.py [311.0 B]
serve.py [503.0 B]
fasttext3.bin [3.2 GB]
fastext_3.py [732.0 B]
val.py [262.0 B]
fastext_2.py [731.0 B]
client.py [207.0 B]
fasttext2.bin [3.2 GB]
📁 📁 .idea
📁 📁 inspectionProfiles
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Project_Default.xml [2.8 KB]
modules.xml [276.0 B]
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05-code_edit.iml [386.0 B]
📁 📁 05-剪枝
LeNet-prune.py [4.0 KB]
📁 📁 03-bert
📁 📁 data
📁 📁 data1
class.txt [83.0 B]
dev.txt [538.4 KB]
test.txt [538.7 KB]
📁 📁 bert_pretrain
pytorch_model.bin [392.5 MB]
vocab.txt [107.0 KB]
bert_config.json [520.0 B]
📁 📁 src
📁 📁 saved_dic
bert.pt [390.2 MB]
📁 📁 saved_dic1
bert_quantized.pt [145.5 MB]
📁 📁 __pycache__
utils.cpython-37.pyc [4.5 KB]
train_eval.cpython-37.pyc [4.2 KB]
📁 📁 models
📁 📁 __pycache__
bert.cpython-37.pyc [2.5 KB]
bert.py [2.7 KB]
predict.py [1.3 KB]
server.py [1.6 KB]
train_eval.py [5.6 KB]
run.py [1.8 KB]
client.py [455.0 B]
utils.py [5.7 KB]
run1.py [1.7 KB]
📁 📁 images
image-20231126091957691.png [53.8 KB]
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📁 📁 06-简历内容
项目案例.md [4.1 KB]
项目文档.md [2.9 KB]
NLP求职--自我介绍以及项目描述参考模板.docx [18.0 KB]
📁 📁 01-讲义
📁 📁 site
📁 📁 assets
📁 📁 images
favicon.png [1.8 KB]
📁 📁 stylesheets
palette.ecc896b0.min.css [12.0 KB]
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palette.ecc896b0.min.css.map [3.6 KB]
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📁 📁 javascripts
📁 📁 workers
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📁 📁 lunr
📁 📁 min
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📁 📁 search
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01-项目背景.html [16.6 KB]
05-fasttext实现.html [65.3 KB]
04-随机森林案例.html [23.5 KB]
index.html [12.9 KB]
06-bert模型.html [130.6 KB]
09-模型蒸馏实践.html [158.9 KB]
sitemap.xml [109.0 B]
07-模型量化.html [30.1 KB]
02-数据集介绍.html [21.9 KB]
08-模型蒸馏.html [19.4 KB]
10-模型剪枝.html [71.6 KB]
03-数据集分析.html [31.0 KB]
📁 📁 03-code
📁 📁 .idea
📁 📁 inspectionProfiles
Project_Default.xml [2.8 KB]
profiles_settings.xml [174.0 B]
workspace.xml [15.7 KB]
modules.xml [266.0 B]
03-code.iml [443.0 B]
misc.xml [195.0 B]
.gitignore [176.0 B]
📁 📁 01-data
📁 📁 data
class.txt [83.0 B]
test.txt [538.7 KB]
stopwords.txt [5.1 KB]
dev.txt [538.4 KB]
📁 📁 05-bert_distil
📁 📁 data
📁 📁 bert_pretrain
bert_config.json [520.0 B]
pytorch_model.bin [392.5 MB]
vocab.txt [107.0 KB]
📁 📁 data
vocab.pkl [73.3 KB]
test.txt [538.7 KB]
dev.txt [538.4 KB]
class.txt [82.0 B]
📁 📁 src
📁 📁 __pycache__
utils.cpython-36.pyc [5.6 KB]
utils.cpython-37.pyc [5.6 KB]
train_eval.cpython-36.pyc [6.4 KB]
train_eval.cpython-37.pyc [6.4 KB]
📁 📁 models
📁 📁 __pycache__
bert.cpython-37.pyc [2.3 KB]
bert.cpython-36.pyc [2.2 KB]
textCNN.cpython-36.pyc [2.9 KB]
textCNN.cpython-37.pyc [2.9 KB]
bert.py [2.6 KB]
textCNN.py [2.7 KB]
📁 📁 saved_dict
textCNN.pt [8.1 MB]
textCNN_8989.pt [10.8 MB]
bert.pt [390.2 MB]
textCNN_9125.pt [22.0 MB]
utils.py [9.2 KB]
train_eval.py [10.7 KB]
run.py [3.0 KB]
📁 📁 03-fast_text
📁 📁 data
📁 📁 data
dev_fast.txt [864.9 KB]
dev.txt [538.4 KB]
test_fast.txt [865.6 KB]
stopwords.txt [5.1 KB]
test.txt [538.7 KB]
train_fast.txt [15.2 MB]
test_fast1.txt [778.1 KB]
preprocess.py [1.6 KB]
dev_fast1.txt [777.4 KB]
class.txt [83.0 B]
preprocess1.py [1.6 KB]
📁 📁 __pycache__
fast_text_3.py [1.8 KB]
app.py [1.1 KB]
toutiao_fasttext_1699862718.bin [764.8 MB]
test.py [522.0 B]
fast_text_2.py [1.8 KB]
fast_text.py [400.0 B]
toutiao_fasttext_1699865297.bin [810.1 MB]
📁 📁 02-random_forest
📁 📁 data
📁 📁 data
stopwords.txt [5.1 KB]
test.txt [538.7 KB]
class.txt [83.0 B]
train_new.csv [21.2 MB]
dev.txt [538.4 KB]
analysis.py [1.5 KB]
random_forest.py [1.1 KB]
📁 📁 06-model_pruning
demo3.py [2.0 KB]
demo1.py [4.8 KB]
demo2.py [3.2 KB]
📁 📁 04-bert
📁 📁 data
📁 📁 data1
dev.txt [538.4 KB]
test.txt [538.7 KB]
class.txt [83.0 B]
📁 📁 bert_pretrain
pytorch_model.bin [392.5 MB]
vocab.txt [107.0 KB]
bert_config.json [520.0 B]
📁 📁 __pycache__
📁 📁 src
📁 📁 saved_dic
bert.pt [390.2 MB]
📁 📁 saved_dic1
bert_quantized.pt [145.5 MB]
📁 📁 __pycache__
utils.cpython-37.pyc [4.9 KB]
train_eval.cpython-37.pyc [4.2 KB]
📁 📁 models
📁 📁 __pycache__
textCNN.cpython-36.pyc [3.0 KB]
bert.cpython-37.pyc [2.5 KB]
bert.py [3.2 KB]
run1.py [1.7 KB]
run.py [1.4 KB]
predict.py [2.4 KB]
train_eval.py [5.6 KB]
utils.py [5.7 KB]
app.py [2.3 KB]
demo.py [444.0 B]
📁 📁 02-data
📁 📁 data
dev.txt [538.4 KB]
test.txt [538.7 KB]
class.txt [83.0 B]
📁 📁 课前说明.mindnode
📁 📁 resources
7BDD87EE-7DE2-41A2-9C41-4CF85D2E14C3.png [50.3 KB]
📁 📁 style.mindnodestyle
contents.xml [6.4 KB]
metadata.plist [391.0 B]
📁 📁 QuickLook
Preview.jpg [212.3 KB]
viewState.plist [149.0 B]
contents.xml [27.6 KB]
requirements.txt [420.0 B]
1.面试中的问题？怎么复习？应届生没经验？.md [1.9 KB]
每日回顾.md [3.0 KB]
📁 📁 阶段3-数据处理与统计分析
📁 📁 day07
📁 📁 代码
📁 📁 data
tips.csv [7.8 KB]
会员消费报表.xlsx [12.7 MB]
全国销售订单数量表.xlsx [239.9 KB]
weight_loss.csv [631.0 B]
门店信息表.XLSX [67.2 KB]
会员信息查询.xlsx [65.7 MB]
09_数据分组.ipynb [62.4 KB]
08_apply自定义函数.ipynb [41.4 KB]
10_零售会员分析和数据透视表.ipynb [604.9 KB]
📁 📁 笔记
📁 📁 assets
image-20230903111822014.png [7.7 KB]
image-20230903154528292.png [85.7 KB]
image-20230903161136371.png [22.7 KB]
image-20230903161157708.png [32.4 KB]
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image-20230903154402405.png [35.6 KB]
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image-20230903161416302.png [7.3 KB]
image-20230903161033577.png [66.2 KB]
image-20230903121614962.png [22.9 KB]
笔记.md [17.2 KB]
DataFrame.xmind [240.1 KB]
📁 📁 day03
📁 📁 软件
mysql-installer-community-8.0.32.0.msi [437.3 MB]
📁 📁 笔记
📁 📁 assets
image-20230829115134383.png [32.6 KB]
image-20230829150203974.png [8.1 KB]
image-20230829111330625.png [129.2 KB]
image-20230829115049856.png [59.2 KB]
image-20230829105606070.png [21.2 KB]
image-20230829145953734.png [18.4 KB]
SQL.xmind [200.2 KB]
笔记.md [15.9 KB]
📁 📁 资料
📁 📁 images
image-20211203181150680.png [350.2 KB]
image-20211207180406126.png [66.3 KB]
image-20211207082501442.png [457.5 KB]
image-20211207082516886.png [339.5 KB]
image-20211203181137386.png [464.8 KB]
03-使用CASE WHEN和GROUP BY将数据分组.md [44.1 KB]
README.md [16.0 B]
报表项目数据.sql [299.3 KB]
02-使用SQL进行数据汇总.md [21.2 KB]
01-数据介绍.md [80.6 KB]
📁 📁 day04
📁 📁 软件
Anaconda3-2023.07-1-MacOSX-arm64.pkg [628.1 MB]
Anaconda3-2023.07-1-Windows-x86_64.exe [893.8 MB]
📁 📁 资料
📁 📁 assets
image-20211120185726198-1681494748457-31.png [78.4 KB]
image-20220522001230496-1681494804794-35.png [85.0 KB]
窗口函数-1-1681494334890-1.png [12.2 KB]
image-20211120185432992-1681494639711-23.png [115.1 KB]
image-20211120192558097-1681495023069-49.png [66.0 KB]
窗口函数关系表1-1681494467004-7.png [85.7 KB]
window-functions-window-functions-part2-ex4-1681494538411-15.gif [19.7 KB]
image-20211128151458381-1681494587121-17.png [161.1 KB]
image-20211120192312940-1681494851671-39.png [66.4 KB]
image-20211120184808370-1681494496087-9.png [122.7 KB]
窗口函数关系表3-1681494817137-37.png [95.6 KB]
image-20211120192532599-1681495006502-47.png [53.9 KB]
image-20211120184834028-1681494507574-11.png [17.1 KB]
image-20211120184857846-1681494517091-13.png [91.0 KB]
image-20211120191843948-1681494792335-33.png [109.4 KB]
image-20211128160655951-1681494947335-43.png [66.4 KB]
image-20211120192628583-1681495059516-51.png [86.8 KB]
image-20211120185552478-1681494672000-25.png [30.4 KB]
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image-20211120192508072-1681494967304-45.png [53.9 KB]
image-20211120185512794-1681494622289-21.png [157.2 KB]
image-20211120192344976-1681494897874-41.png [80.4 KB]
窗口函数-2-1681494371801-3.png [2.3 KB]
image-20211120185653806-1681494725399-29.png [22.4 KB]
image-20211120185237870-1681494602759-19.png [109.8 KB]
image-20211120185620476-1681494692001-27.png [42.8 KB]
nobel_prizes.csv [124.4 KB]
movie.csv [1.5 MB]
window.md [24.1 KB]
📁 📁 代码
📁 📁 data
nobel_prizes.csv [124.4 KB]
movie.csv [1.5 MB]
01_numpy.ipynb [32.1 KB]
02_Pandas入门.ipynb [19.4 KB]
📁 📁 笔记
📁 📁 assets
image-20230830112336326.png [55.2 KB]
image-20230830111550275.png [179.2 KB]
image-20230830170240967.png [99.4 KB]
image-20230830112114351.png [39.4 KB]
image-20230830112957911.png [70.1 KB]
image-20230830111750862.png [77.3 KB]
image-20230830111859383.png [41.8 KB]
笔记.md [12.7 KB]
📁 📁 课件
03 Pandas 数据结构.pptx [3.3 MB]
01_02 Python数据分析简介&环境搭建.pptx [4.3 MB]
02-Numpy.pptx [1.1 MB]
📁 📁 day01
📁 📁 资料
📁 📁 01-VMware虚拟机（必装）
📁 📁 VMware
📁 📁 vmware16pro
序列号16.txt [95.0 B]
VMware-workstation-full-16.1.0-17198959.exe [621.5 MB]
📁 📁 vmware15
VMware Keygen 14-15.exe [251.5 KB]
VMware-workstation-full-15.5.0-14665864.exe [541.1 MB]
01-VMware15安装.doc [412.5 KB]
VMware-Fusion-13.0.1-21139760_universal.dmg [672.1 MB]
Mac系统VMWare虚拟机 NAT网络设置(固定IP).pdf [627.7 KB]
mac激活码.txt [423.0 B]
📁 📁 02-ssh工具
finalshell_install.exe [83.8 MB]
finalshell_install.pkg [120.4 MB]
📁 📁 笔记
📁 📁 assets
image-20230826180220021.png [88.7 KB]
image-20230826162219370.png [24.4 KB]
image-20230826170352512.png [12.1 KB]
image-20230826162115171.png [64.5 KB]
image-20230826194019072.png [12.1 KB]
image-20230826180448863.png [47.1 KB]
image-20230826181620057.png [7.1 KB]
image-20230826162128323.png [45.0 KB]
image-20230826175520613.png [58.2 KB]
image-20230826180118118.png [52.5 KB]
image-20230826193857135.png [1.9 KB]
image-20230826162045047.png [37.6 KB]
笔记.md [7.7 KB]
📁 📁 课件
4-Linux实用操作.pptx [14.4 MB]
1-初识Linux.pptx [15.2 MB]
2-Linux基础命令.pptx [12.1 MB]
3-用户和权限.pptx [3.8 MB]
📁 📁 画图
用户权限.png [46.2 KB]
test.txt [56.0 B]
📁 📁 day06
📁 📁 代码
📁 📁 data
city_day.csv [2.5 MB]
stocks_2017.csv [108.0 B]
nobel_prizes.csv [124.4 KB]
concat_2.csv [56.0 B]
movie5.pkl [3.3 KB]
movie.csv [1.5 MB]
survey_visited1.csv [165.0 B]
concat_1.csv [56.0 B]
movie5_noindex.tsv [1.9 KB]
movie5.csv [1.9 KB]
stocks_2018.csv [71.0 B]
titanic_train.csv [59.8 KB]
concat_3.csv [64.0 B]
movie5_noindex.csv [1.9 KB]
movie5.xlsx [6.4 KB]
gapminder.tsv [80.1 KB]
chinook.db [864.0 KB]
LJdata.csv [702.3 KB]
survey_visited.csv [168.0 B]
scientists.csv [433.0 B]
titanic_test.csv [28.0 KB]
stocks_2016.csv [65.0 B]
05_租房数据练习.ipynb [100.2 KB]
06_数据连接.ipynb [53.7 KB]
07_缺失值处理.ipynb [196.8 KB]
04_Pandas数据分析练习.ipynb [34.1 KB]
01_numpy.ipynb [32.1 KB]
08_apply自定义函数.ipynb [32.6 KB]
02_Pandas入门.ipynb [297.6 KB]
test.ipynb [3.0 KB]
03_DataFrame数据分析入门.ipynb [94.9 KB]
📁 📁 笔记
📁 📁 assets
image-20230902151641791.png [52.3 KB]
image-20230902151658320.png [31.1 KB]
image-20230902105652355-1693643530189-1.png [18.1 KB]
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image-20230902105746580.png [71.1 KB]
image-20230902105652355.png [18.1 KB]
image-20230902105810235.png [10.2 KB]
笔记.md [12.6 KB]
DataFrame.xmind [241.3 KB]
📁 📁 数据
city_day.csv [2.5 MB]
chinook.db [864.0 KB]
stocks_2016.csv [65.0 B]
sales.xlsx [15.3 MB]
concat_1.csv [56.0 B]
concat_3.csv [64.0 B]
stocks_2018.csv [71.0 B]
titanic_test.csv [28.0 KB]
tips.csv [7.8 KB]
titanic_train.csv [59.8 KB]
weight_loss.csv [631.0 B]
survey_visited.csv [168.0 B]
stocks_2017.csv [108.0 B]
concat_2.csv [56.0 B]
LJdata.csv [702.3 KB]
📁 📁 课件
07 缺失数据处理.pptx [525.4 KB]
09 数据分组.pptx [1.3 MB]
10 数据透视表.pptx [818.7 KB]
08 apply自定义函数.pptx [702.5 KB]
06 数据组合.pptx [1013.7 KB]
📁 📁 作业
chinook.png [233.9 KB]
今日作业.md [1.2 KB]
📁 📁 驱动sqlite
sqlite-jdbc-3.43.0.0.jar [12.6 MB]
📁 📁 day05
📁 📁 数据
📁 📁 课件
05 Pandas数据分析入门.pptx [1.0 MB]
03 Pandas 数据结构.pptx [3.3 MB]
04 Pandas DataFrame入门.pptx [1.8 MB]
📁 📁 代码
📁 📁 data
scientists.csv [433.0 B]
gapminder.tsv [80.1 KB]
📁 📁 .idea
.name [21.0 B]
workspace.xml [1.2 KB]
02_Pandas入门.ipynb [297.6 KB]
04_Pandas数据分析练习.ipynb [34.1 KB]
03_DataFrame数据分析入门.ipynb [94.9 KB]
📁 📁 笔记
📁 📁 assets
image-20230831165455488.png [17.8 KB]
image-20230831101513455.png [8.3 KB]
image-20230831101419159.png [9.3 KB]
笔记.md [11.1 KB]
Pandas&numpy.xmind [219.0 KB]
📁 📁 day08
📁 📁 课件
13 Pandas绘图.pptx [918.5 KB]
12 Matplotlib绘图.pptx [719.3 KB]
11 datetime数据类型.pptx [841.0 KB]
📁 📁 笔记
📁 📁 assets
image-20230905173528546.png [27.2 KB]
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image-20230905183506956.png [53.4 KB]
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image-20230905121916778.png [29.7 KB]
image-20230905112147497.png [36.1 KB]
image-20230905164509887.png [27.0 KB]
image-20230905173433840.png [22.5 KB]
image-20230905181355351.png [25.5 KB]
image-20230905173404220.png [65.1 KB]
image-20230905173457270.png [27.5 KB]
image-20230905164943956.png [52.2 KB]
DataFrame.xmind [248.3 KB]
笔记.md [17.5 KB]
📁 📁 代码
📁 📁 data
winemag-data_first150k.csv [47.5 MB]
TSLA.csv [122.5 KB]
banklist.csv [45.1 KB]
country_timeseries.csv [5.5 KB]
anscombe.csv [556.0 B]
crime.csv [42.9 MB]
13_Pandas的数据可视化.ipynb [191.7 KB]
12_Matplotlib绘图.ipynb [273.0 KB]
11_日期时间类型.ipynb [335.2 KB]
10_零售会员分析和数据透视表.ipynb [624.3 KB]
📁 📁 day09
📁 📁 笔记
📁 📁 assets
image-20230906113643962.png [47.7 KB]
image-20230906094134783.png [126.3 KB]
image-20230906101054532.png [62.0 KB]
image-20230906093536874.png [27.3 KB]
image-20230906113628456.png [47.8 KB]
image-20230906145827647.png [48.8 KB]
image-20230906112922023.png [14.9 KB]
image-20230906093943703.png [34.8 KB]
image-20230906093440683.png [37.3 KB]
image-20230906094124691.png [56.6 KB]
image-20230906144553264.png [107.6 KB]
image-20230906093914494.png [34.1 KB]
image-20230906113405510.png [42.2 KB]
image-20230906144617562.png [107.3 KB]
image-20230906111132723.png [19.1 KB]
image-20230906093431827.png [14.1 KB]
image-20230906113259638.png [14.4 KB]
image-20230906101046248.png [16.2 KB]
image-20230906102157332.png [10.7 KB]
DataFrame.xmind [232.9 KB]
笔记.md [45.7 KB]
📁 📁 代码
📁 📁 数据
sales.xlsx [15.3 MB]
15_RFM案例.ipynb [63.4 KB]
13_Pandas的数据可视化.ipynb [779.2 KB]
14_seaborn可视化.ipynb [1.1 MB]
📁 📁 课件
15 综合案例_RFM会员价值度模型案例.pptx [1.0 MB]
14 Seaborn绘图.pptx [1.4 MB]
📁 📁 day10
📁 📁 笔记
DataFrame.xmind [264.2 KB]
笔记.md [6.8 KB]
📁 📁 课件
📁 📁 assets
appstore10.png [30.6 KB]
image-20230424015521067.png [28.3 KB]
appstore4.png [8.7 KB]
appstore2.png [9.7 KB]
appstore3.png [5.3 KB]
appstore5.png [10.2 KB]
appstore6.png [12.6 KB]
appstore1.png [32.6 KB]
appstore8.png [7.4 KB]
appstore9.png [63.0 KB]
app_plot10.png [37.4 KB]
image-20230424020619932.png [87.6 KB]
appstore7.png [32.9 KB]
Appstore数据分析.md [12.0 KB]
优衣库数据分析需求.md [1.1 KB]
优衣库销售数据分析.md [44.6 KB]
📁 📁 代码
📁 📁 data
applestore.csv [590.1 KB]
uniqlo.csv [1.2 MB]
16_appstore数据分析.ipynb [487.8 KB]
17_优衣库销售数据分析.ipynb [73.1 KB]
📁 📁 day02
📁 📁 笔记
📁 📁 assets
image-20230827110812682.png [103.4 KB]
image-20230827094628156.png [4.1 KB]
image-20230827110514160.png [82.5 KB]
image-20230827151207681.png [77.5 KB]
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image-20230827151102624.png [58.3 KB]
image-20230827151423370.png [45.5 KB]
image-20230827151002359.png [79.3 KB]
image-20230827151250721.png [47.9 KB]
笔记.md [10.9 KB]
Linux.xmind [173.2 KB]
📁 📁 课件
2-第二章-MySQL基础.pptx [6.5 MB]
code.txt [4.5 KB]
📁 📁 软件
pycharm-professional-2023.2.exe [514.6 MB]
8.0.25.rar [2.2 MB]
📁 📁 阶段014-亿图人脸支付项目
📁 📁 06.CV参考简历
📁 📁 cv模拟面试题集合答案版
目标检测分割面试.docx [1.9 MB]
面试题_计算机视觉带答案.docx [4.6 MB]
图像处理面试题.docx [23.3 MB]
简历8.pdf [390.2 KB]
简历4.pdf [1.8 MB]
简历3.pdf [246.1 KB]
简历7.pdf [97.6 KB]
简历6.pdf [179.3 KB]
简历1.pdf [301.6 KB]
简历9.pdf [174.2 KB]
简历5.pdf [166.5 KB]
02.code.zip [12.4 GB]
📁 📁 其他
📁 📁 串讲
📁 📁 北京AI17期AI医生串讲
03 代码文件说明.mkv [16.3 MB]
01 流程代码串讲.mkv [110.9 MB]
05 调试以及其他问题.mkv [233.7 MB]
04 查看日志方法.mkv [39.0 MB]
02 项目部署串讲.mkv [42.2 MB]
AI模型部署-17期加密.zip [1.4 GB]
李刚#AI关系抽取项目#17期加密.zip [5.7 GB]
📁 📁 录屏软件
📁 📁 EVCapture
📁 📁 Uninstaller
unins000.dat [49.3 KB]
unins000.exe [1.1 MB]
📁 📁 bin
📁 📁 ui
NetWork.dll [85.5 KB]
EVView.dll [132.0 KB]
MainWindow.dll [1.1 MB]
📁 📁 normal
Login.dll [87.0 KB]
Skin.dll [193.5 KB]
EVCmd.dll [434.0 KB]
📁 📁 plugin
📁 📁 LocalLive
📁 📁 Nginx_EV
📁 📁 html
📁 📁 bd
…（已达最大深度 10 层，子目录未展开）
📁 📁 js
…（已达最大深度 10 层，子目录未展开）
📁 📁 css
…（已达最大深度 10 层，子目录未展开）
📁 📁 img
…（已达最大深度 10 层，子目录未展开）
mobile.html [393.0 B]
index.html [7.8 KB]
stat.xsl [11.1 KB]
crossdomain.xml [79.0 B]
ParsedQueryString.js [3.0 KB]
📁 📁 temp
📁 📁 proxy_temp
…（已达最大深度 10 层，子目录未展开）
📁 📁 client_body_temp
…（已达最大深度 10 层，子目录未展开）
📁 📁 fastcgi_temp
…（已达最大深度 10 层，子目录未展开）
📁 📁 hls
…（已达最大深度 10 层，子目录未展开）
readme.txt [40.0 B]
📁 📁 conf
mime.types [3.9 KB]
koi-utf [2.8 KB]
win-utf [3.5 KB]
fastcgi.conf [1.1 KB]
scgi_params [636.0 B]
fastcgi_params [1007.0 B]
nginx-m2.conf [776.0 B]
uwsgi_params [664.0 B]
koi-win [2.2 KB]
nginx.conf [3.2 KB]
📁 📁 scgi_temp
📁 📁 logs
error.log
access.log
📁 📁 uwsgi_temp
readme.txt [538.0 B]
nginx-ev-stop.bat [20.0 B]
nginx-ev.exe [2.5 MB]
LocalLive.dll [215.0 KB]
TextMark.dll [102.5 KB]
ImageMark.dll [90.0 KB]
CpCamera.dll [222.5 KB]
CpScreen.dll [62.5 KB]
📁 📁 ev
MixAudio.dll [34.0 KB]
AVEncode.dll [170.0 KB]
AudioSysEx.dll [94.5 KB]
AudioMic.dll [47.5 KB]
MixVideo.dll [33.5 KB]
AudioSpeex.dll [191.0 KB]
AudioSysEx_win10.dll [57.0 KB]
📁 📁 imageformats
qjpeg4.dll [231.0 KB]
qmng4.dll [355.5 KB]
qsvg4.dll [28.0 KB]
qico4.dll [36.0 KB]
qtga4.dll [28.0 KB]
qtiff4.dll [363.5 KB]
qgif4.dll [34.5 KB]
Mp4Fix.exe [263.7 KB]
libopencv_core310.dll [3.1 MB]
swresample-1.dll [275.5 KB]
libbz2-1.dll [74.8 KB]
FFmpeg.exe [338.2 KB]
libopencv_videoio310.dll [425.4 KB]
avcodec-56.dll [21.3 MB]
libwinpthread-1.dll [47.5 KB]
framecore4.1.dll [389.0 KB]
postproc-53.dll [128.5 KB]
EVCapture.exe [365.7 KB]
logger.dll [15.5 KB]
msvcp140.dll [443.3 KB]
libopencv_imgproc310.dll [3.5 MB]
WhiteBoard.exe [250.7 KB]
QRViewer.dll [115.5 KB]
VCInfoEx.dll [531.5 KB]
vcruntime140.dll [81.3 KB]
FLGetCpuID.dll [519.5 KB]
avdevice-56.dll [1.3 MB]
QtXml4.dll [352.0 KB]
libopencv_imgcodecs310.dll [3.0 MB]
avformat-56.dll [5.7 MB]
wasapi_plugin_console.exe [48.7 KB]
libgcc_s_dw2-1.dll [114.5 KB]
QtGui4.dll [9.6 MB]
Tools.exe [35.2 KB]
libstdc++-6.dll [948.0 KB]
EVPlayer3Lib.dll [383.5 KB]
EVPlayer.exe [1.4 MB]
swscale-3.dll [476.0 KB]
QtMultimedia4.dll [136.0 KB]
QtCore4.dll [2.8 MB]
EVUpdate.exe [86.7 KB]
Network.dll [1.1 MB]
WmDll.dll [14.5 KB]
avutil-54.dll [483.5 KB]
libopencv_highgui310.dll [148.0 KB]
QtNetwork4.dll [1.3 MB]
evdx.dll [26.0 KB]
evBridge482.dll [332.5 KB]
avfilter-5.dll [2.3 MB]
📁 📁 data
📁 📁 audio
📁 📁 music
birds_outro.mp3 [292.0 KB]
ambient_white_dryforest.mp3 [652.0 KB]
ambient_construction.mp3 [882.2 KB]
ambient_red_savannah.mp3 [758.6 KB]
birds_intro.mp3 [357.5 KB]
ambient_green_jungleish.mp3 [1.6 MB]
title_theme.mp3 [1.6 MB]
ambient_city.mp3 [955.1 KB]
game_complete.mp3 [283.4 KB]
birds_boss.mp3 [146.9 KB]
level_complete.mp3 [69.1 KB]
funky_theme.mp3 [1.7 MB]
📁 📁 sfx
bird misc a11.wav [67.8 KB]
bird 01 collision a3.wav [41.0 KB]
bird next military a2.wav [175.9 KB]
bigbrother_fly.wav [74.3 KB]
bird 02 collision a2.wav [41.9 KB]
bird shot-a2.wav [62.9 KB]
bird misc a2.wav [37.6 KB]
bird misc a4.wav [37.2 KB]
bird 02 flying.wav [90.6 KB]
bird 01 collision a1.wav [47.4 KB]
bird 03 collision a3.wav [30.1 KB]
bird 03 select.wav [44.0 KB]
bird 05 flying.wav [115.0 KB]
bird 03 collision a1.wav [38.1 KB]
bird 04 flying.wav [124.8 KB]
bird 05 select.wav [77.8 KB]
bird 03 collision a2.wav [33.6 KB]
bird misc a10.wav [61.5 KB]
bird 05 collision a1.wav [48.9 KB]
bird 04 select.wav [102.5 KB]
bird 03 flying.wav [154.5 KB]
bigbrother_select.wav [63.1 KB]
bird 05 collision a5.wav [35.2 KB]
bird 01 select.wav [98.1 KB]
bird misc a9.wav [86.6 KB]
bird destroyed.wav [62.6 KB]
bird 01 collision a2_low.wav [54.0 KB]
bird misc a1.wav [44.6 KB]
bird misc a5.wav [45.6 KB]
bird_06_flying.wav [133.6 KB]
bird pushing egg out.wav [46.5 KB]
bird 01 collision a1_low.wav [47.5 KB]
balloon_pop.wav [21.9 KB]
bird 01 collision a2.wav [53.9 KB]
bird 04 collision a2.wav [32.0 KB]
bird 04 collision a1.wav [26.7 KB]
bigbrother_awakens.wav [128.7 KB]
bird 02 collision a4.wav [50.2 KB]
bird misc a3.wav [54.6 KB]
bird 05 collision a2.wav [44.3 KB]
bird 02 select.wav [72.9 KB]
bird 05 collision a4.wav [40.5 KB]
bird next military a3.wav [158.8 KB]
bird misc a8.wav [63.5 KB]
bird shot-a1.wav [60.8 KB]
bird next military a1.wav [177.8 KB]
bird 01 collision a3_low.wav [41.0 KB]
bird 04 collision a4.wav [48.6 KB]
bird 02 collision a5.wav [26.1 KB]
bigbrother_yell.wav [58.6 KB]
bird misc a6.wav [48.4 KB]
bird misc a7.wav [68.8 KB]
bird misc a12.wav [77.2 KB]
bird 04 collision a3.wav [27.0 KB]
bird 02 collision a1.wav [50.2 KB]
bird 01 collision a4.wav [66.6 KB]
bird shot-a3.wav [42.5 KB]
bird 01 flying.wav [145.9 KB]
ball_bounce.wav [28.2 KB]
bird 03 collision a5.wav [31.4 KB]
bird 05 collision a3.wav [46.2 KB]
bird 02 collision a3.wav [46.1 KB]
bird 03 collision a4.wav [25.7 KB]
bird 01 collision a4_low.wav [66.6 KB]
EVCapture.exe [91.9 KB]
logo.ico [16.6 KB]
ReadMe.txt [5.1 KB]
mac录屏软件obs.txt
OBS官网.txt [23.0 B]
FastStone Capture7.5.zip [3.6 MB]
win下录屏软FsCapture.txt
OBS-Studio-29.1.3-Full-Installer-x64.exe [127.9 MB]
FSCapture_7.7.rar [2.5 MB]
📁 📁 2、版本控制Git
Git讲义.pdf [2.8 MB]
01-版本控制Git.rar [580.7 MB]
📁 📁 ftp工具
📁 📁 11_FileZilla
📁 📁 Mac
FileZilla_3.41.2_macosx-x86.app.tar.bz2 [10.2 MB]
📁 📁 windows
FileZilla_3.41.2_win64-setup.exe [7.6 MB]
📁 📁 02-ssh工具
📁 📁 VMware
📁 📁 vmware16pro
VMware-workstation-full-16.1.0-17198959.exe [621.5 MB]
序列号16.txt [95.0 B]
📁 📁 vmware15
01-VMware15安装.doc [412.5 KB]
VMware Keygen 14-15.exe [251.5 KB]
VMware-workstation-full-15.5.0-14665864.exe [541.1 MB]
Mac系统VMWare虚拟机 NAT网络设置(固定IP).pdf [627.7 KB]
VMware-Fusion-13.0.1-21139760_universal.dmg [672.1 MB]
mac激活码.txt [423.0 B]
finalshell_install.exe [83.8 MB]
finalshell_install.pkg [120.4 MB]
mysql-installer-community-8.0.32.0.msi [437.3 MB]
📁 📁 上课笔记
2.5笔记.pdf [477.0 KB]
1.6多任务编程-课堂笔记.pdf [4.3 MB]
0.7day07笔记.pdf [1.5 MB]
3.6朴素贝叶斯.pdf [712.4 KB]
机器学习阶段复习.pdf [81.4 KB]
3.3逻辑回归.pdf [1.7 MB]
0.1day01笔记.pdf [5.9 MB]
1.3学员管理系统(面向对象).pdf [1.1 MB]
1.9排序-笔记.pdf [3.8 MB]
0.5day05笔记.pdf [846.0 KB]
3.9支持向量机.pdf [4.1 MB]
2.4笔记.pdf [947.1 KB]
课前说明.pdf [75.8 KB]
1.4闭包和装饰器.pdf [3.7 MB]
2.6笔记.pdf [672.3 KB]
1.1Python面向对象基础.pdf [11.7 MB]
0.8day08笔记.pdf [789.9 KB]
2.2笔记.pdf [901.2 KB]
1.10二叉树-笔记.pdf [9.7 MB]
3.1机器学习概述.pdf [13.4 MB]
3.8聚类.pdf [4.1 MB]
0.6day06笔记.pdf [1.1 MB]
3.7特征降维.pdf [1.6 MB]
0.4day04笔记.pdf [866.3 KB]
2.8笔记.pdf [930.5 KB]
2.7笔记.pdf [890.8 KB]
3.4集成学习.pdf [7.6 MB]
1.5网络编程-课堂笔记.pdf [10.8 MB]
3.2KNN算法.pdf [5.1 MB]
0.2day02笔记.pdf [2.2 MB]
2.9笔记.pdf [1.3 MB]
2.3笔记.pdf [677.8 KB]
2.10笔记.pdf [390.5 KB]
1.2Python面向对象高级.pdf [2.8 MB]
1.8数据结构与算法.pdf [3.8 MB]
3.5决策树.pdf [5.4 MB]
2.1笔记.pdf [824.8 KB]
0.3day03笔记.pdf [773.3 KB]
1.7Python高级语法与正则表达式.pdf [2.3 MB]
Git资料.zip [49.0 MB]
📁 📁 阶段4-机器学习与多场景项目实战
📁 📁 10-机器学习案例
📁 📁 02-代码
test.csv [26.6 MB]
test.csv.zip [4.0 MB]
train.csv [11.9 MB]
train.csv.zip [1.7 MB]
📁 📁 01-案例介绍
📁 📁 images
image-20230907193254546.png [482.8 KB]
image-20230907200341280.png [88.4 KB]
image-20230907200400379.png [84.8 KB]
otto案例介绍 -- Otto Group Product Classification Challenge.md [1.1 KB]
📁 📁 课前说明.mindnode
📁 📁 style.mindnodestyle
metadata.plist [391.0 B]
contents.xml [6.4 KB]
📁 📁 QuickLook
Preview.jpg [201.1 KB]
📁 📁 resources
viewState.plist [147.0 B]
contents.xml [53.9 KB]
📁 📁 02-KNN算法
📁 📁 06-今日总结
📁 📁 KNN算法.mindnode
📁 📁 style.mindnodestyle
contents.xml [6.4 KB]
metadata.plist [391.0 B]
📁 📁 QuickLook
Preview.jpg [161.1 KB]
📁 📁 resources
contents.xml [52.3 KB]
viewState.plist [153.0 B]
📁 📁 05-作业
作业.md [3.1 KB]
📁 📁 02-笔记
📁 📁 images
image-20230831155813699.png [491.4 KB]
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image-20230831152503338.png [338.5 KB]
image-20230831161125003.png [59.5 KB]
image-20230831145250261.png [27.5 KB]
image-20230831143443988.png [84.6 KB]
KNN算法.md [11.5 KB]
📁 📁 03-代码
knn.pth [201.2 MB]
03-knn_iris.py [1.4 KB]
手写数字识别.csv [73.2 MB]
02-特征预处理.py [365.0 B]
01-KNN API 实验.py [472.0 B]
05-knn digit.py [1.2 KB]
demo.png [252.0 B]
04-GridSearchCV.py [1.0 KB]
📁 📁 01-讲义
KNN算法.pptx [9.3 MB]
📁 📁 06-集成学习
📁 📁 05-作业
作业.md [7.0 KB]
📁 📁 06-今日总结
📁 📁 集成学习.mindnode
📁 📁 resources
📁 📁 QuickLook
Preview.jpg [177.0 KB]
📁 📁 style.mindnodestyle
contents.xml [6.5 KB]
metadata.plist [394.0 B]
viewState.plist [178.0 B]
contents.xml [59.2 KB]
📁 📁 03-代码
📁 📁 data
📁 📁 titanic
gender_submission.csv [3.2 KB]
test.csv [28.0 KB]
train.csv [59.8 KB]
wine0501.csv [11.2 KB]
红酒品质分类.csv [98.6 KB]
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
Project_Default.xml [2.8 KB]
workspace.xml [9.8 KB]
misc.xml [195.0 B]
03-代码.iml [284.0 B]
modules.xml [270.0 B]
.gitignore [176.0 B]
02-adaboost.py [575.0 B]
03-GBDT.py [819.0 B]
红酒品质分类_test.csv [19.9 KB]
04-Xgboost.py [1.2 KB]
01-RandomForest.py [1.3 KB]
红酒品质分类_train.csv [78.3 KB]
📁 📁 02-笔记
📁 📁 images
image-20230917143542540.png [199.1 KB]
33.png [76.7 KB]
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image-20230905235722201.png [335.3 KB]
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11.png [175.1 KB]
boosting3.png [159.1 KB]
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boosting6.png [214.1 KB]
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10.png [62.4 KB]
image-20230906004228553.png [335.3 KB]
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boostin4.png [145.0 KB]
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boosting2.png [126.0 KB]
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02.png [379.2 KB]
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2021-2.png [693.2 KB]
image-20230906170712026.png [565.3 KB]
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48.png [24.2 KB]
boosting5.png [189.2 KB]
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image-20230905235742221.png [467.9 KB]
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2021-1.png [443.0 KB]
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集成学习.md [33.3 KB]
📁 📁 01-讲义
集成学习.pptx [10.7 MB]
📁 📁 04-逻辑回归
📁 📁 06-今日总结
📁 📁 逻辑回归.mindnode
📁 📁 QuickLook
Preview.jpg [195.6 KB]
📁 📁 style.mindnodestyle
contents.xml [6.4 KB]
metadata.plist [391.0 B]
📁 📁 resources
viewState.plist [147.0 B]
contents.xml [18.6 KB]
📁 📁 01-讲义
逻辑回归.pptx [4.7 MB]
📁 📁 02-笔记
📁 📁 images
05.png [12.8 KB]
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逻辑回归.md [15.9 KB]
📁 📁 05-作业
作业.md [2.5 KB]
📁 📁 03-代码
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
Project_Default.xml [2.8 KB]
.gitignore [176.0 B]
03-代码.iml [284.0 B]
modules.xml [270.0 B]
workspace.xml [9.6 KB]
misc.xml [195.0 B]
02- classmetirc.py [1.1 KB]
01-LR cancer.py [912.0 B]
churn.csv [314.2 KB]
03-churn.py [1.1 KB]
breast-cancer-wisconsin.csv [19.6 KB]
📁 📁 01-机器学习概述
📁 📁 01-讲义
机器学习概述.pptx [12.2 MB]
📁 📁 05-作业
作业.md [919.0 B]
📁 📁 02-笔记
📁 📁 images
image-20230831112038043.png [157.5 KB]
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intro2.jpg [407.9 KB]
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机器学习概述.md [13.4 KB]
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📁 📁 03-代码
📁 📁 .idea
📁 📁 inspectionProfiles
Project_Default.xml [2.8 KB]
profiles_settings.xml [174.0 B]
03-代码.iml [284.0 B]
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workspace.xml [1.9 KB]
modules.xml [270.0 B]
📁 📁 06-今日总结
📁 📁 人工智能概述.mindnode
📁 📁 resources
📁 📁 QuickLook
Preview.jpg [146.7 KB]
📁 📁 style.mindnodestyle
metadata.plist [391.0 B]
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📁 📁 03-线性回归
📁 📁 06-今日总结
📁 📁 线性回归.mindnode
📁 📁 resources
📁 📁 QuickLook
Preview.jpg [183.0 KB]
📁 📁 style.mindnodestyle
metadata.plist [391.0 B]
contents.xml [6.4 KB]
contents.xml [42.8 KB]
viewState.plist [148.0 B]
📁 📁 02-笔记
📁 📁 images
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线性回归.md [26.4 KB]
📁 📁 01-讲义
线性回归.pptx [13.2 MB]
📁 📁 03-代码
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
Project_Default.xml [2.8 KB]
.gitignore [176.0 B]
misc.xml [195.0 B]
workspace.xml [7.6 KB]
modules.xml [270.0 B]
03-代码.iml [284.0 B]
03-拟合效果.py [3.3 KB]
breast-cancer-wisconsin.csv [19.6 KB]
02-boston.py [1.2 KB]
波士顿房价xy.csv [38.3 KB]
01-LR API.py [336.0 B]
📁 📁 05-作业
作业.md [2.3 KB]
📁 📁 05-决策树
📁 📁 06-今日总结
📁 📁 决策树.mindnode
📁 📁 style.mindnodestyle
contents.xml [6.5 KB]
metadata.plist [390.0 B]
📁 📁 QuickLook
Preview.jpg [272.7 KB]
📁 📁 resources
viewState.plist [179.0 B]
contents.xml [39.6 KB]
📁 📁 05-作业
📁 📁 images
image-20220530202122503-3913284.png [147.5 KB]
作业.md [4.9 KB]
📁 📁 01-讲义
决策树.pptx [6.4 MB]
📁 📁 03-代码
📁 📁 titanic
train.csv [59.8 KB]
gender_submission.csv [3.2 KB]
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
Project_Default.xml [2.8 KB]
misc.xml [195.0 B]
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03-代码.iml [284.0 B]
.gitignore [176.0 B]
modules.xml [270.0 B]
02-RegressionTree.py [998.0 B]
01-titanic.py [864.0 B]
📁 📁 02-笔记
📁 📁 images
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📁 📁 09-支持向量机SVM
📁 📁 03-代码
📁 📁 __pycache__
plot_util.cpython-37.pyc [1.6 KB]
📁 📁 .idea
📁 📁 inspectionProfiles
Project_Default.xml [2.8 KB]
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.gitignore [176.0 B]
03-RBF.py [1.2 KB]
02-C.py [1.2 KB]
plot_util.py [1.6 KB]
01-svm api.py [846.0 B]
📁 📁 05-作业
📁 📁 06-今日总结
📁 📁 01-讲义
支持向量机SVM.pptx [9.8 MB]
📁 📁 02-笔记
📁 📁 images
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支持向量机.md [14.4 KB]
📁 📁 07-朴素贝叶斯和特征降维
📁 📁 05-作业
作业.md [1.3 KB]
📁 📁 03-代码
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
Project_Default.xml [2.8 KB]
03-代码.iml [284.0 B]
workspace.xml [6.7 KB]
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modules.xml [270.0 B]
misc.xml [195.0 B]
垃圾邮件分类数据.csv [47.8 MB]
02-featureexture.py [684.0 B]
stopwords.txt [11.5 KB]
01-bayes.py [1.1 KB]
书籍评价.csv [540.0 B]
📁 📁 06-今日总结
📁 📁 01-讲义
特征降维.pptx [1.3 MB]
朴素贝叶斯.pptx [1.1 MB]
📁 📁 02-笔记
📁 📁 images
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01.png [72.9 KB]
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特征降维.md [4.3 KB]
朴素贝叶斯.md [7.3 KB]
📁 📁 08-聚类kmeans算法
📁 📁 01-讲义
聚类.pptx [4.6 MB]
📁 📁 03-代码
📁 📁 data
customers.csv [3.9 KB]
factor_returns.csv [309.0 KB]
📁 📁 .idea
📁 📁 inspectionProfiles
Project_Default.xml [2.8 KB]
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modules.xml [270.0 B]
03-customers.py [1.3 KB]
02- kmeans metric.py [954.0 B]
01-kmeans API.py [562.0 B]
📁 📁 02-笔记
📁 📁 images
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聚类.md [10.6 KB]
📁 📁 05-作业
作业.md [5.1 KB]
📁 📁 06-今日总结
📁 📁 聚类算法.mindnode
📁 📁 QuickLook
Preview.jpg [206.4 KB]
📁 📁 style.mindnodestyle
contents.xml [6.4 KB]
metadata.plist [391.0 B]
📁 📁 resources
contents.xml [45.1 KB]
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📁 📁 阶段1-python基础编程
📁 📁 day05
📁 📁 作业
06_tuple.md [2.0 KB]
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📁 📁 代码
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
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02-列表的操作--改.py [408.0 B]
03-列表的反转及排序.py [1.4 KB]
16-容器的公共运算符.py [2.8 KB]
10-字典的定义.py [2.0 KB]
06-元组的定义.py [1.4 KB]
05-列表的推导式.py [2.0 KB]
04-列表的嵌套.py [2.2 KB]
17-容器的公共函数.py [1.7 KB]
15-字典的遍历.py [1.0 KB]
14-字典的操作--删.py [882.0 B]
11-字典的操作--查.py [1.7 KB]
12-字典的操作--增.py [901.0 B]
08-元组的常见操作.py [1.0 KB]
01-列表的操作--删.py [3.2 KB]
07-元组的特性.py [1008.0 B]
13-字典的操作--改.py [942.0 B]
09-set集合的介绍.py [2.7 KB]
📁 📁 笔记
📁 📁 img
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📁 📁 images
1691200452344.png [145.5 KB]
day05笔记.md [27.2 KB]
📁 📁 day07
📁 📁 作业
08-文件操作作业.md [4.1 KB]
📁 📁 笔记
📁 📁 img
image-20220520163117456.png [365.2 KB]
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day07笔记.md [24.0 KB]
📁 📁 代码
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
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modules.xml [271.0 B]
📁 📁 data
12-os模块的简单使用.py [1.9 KB]
shi[备份].txt [94.0 B]
11-相对路径和绝对路径.py [819.0 B]
06-文件的追加.py [1.7 KB]
10-字符集的意义.py [1.8 KB]
08-文件备份案例--字节型备份.py [1.6 KB]
3.txt
erkang[备份].jpg [14.1 KB]
erkang.jpg [14.1 KB]
07-文件备份案例.py [870.0 B]
demo1.txt [44.0 B]
09-文件的读写模式扩展(了解).py [3.1 KB]
02-递归(了解).py [1.9 KB]
shi.txt [94.0 B]
2.txt [402.0 B]
05-文件的写入.py [1.8 KB]
01-lambda表达式.py [2.9 KB]
2[备份].txt [403.0 B]
04-文件的读取.py [3.2 KB]
03-文件读写体验.py [374.0 B]
📁 📁 01-基础讲义
📁 📁 file
📁 📁 3
section.11.0.html [43.7 KB]
section.6.html [36.7 KB]
section.9.html [42.2 KB]
section.4.html [44.5 KB]
section.4.2.html [43.2 KB]
section.5.html [37.0 KB]
section.99.html [34.9 KB]
section.11.4.html [43.7 KB]
index.html [38.3 KB]
section.1.html [38.5 KB]
section.11.1.html [38.5 KB]
section.7.html [57.1 KB]
section.4.1.html [40.2 KB]
section.10.html [42.2 KB]
section.2.html [36.3 KB]
📁 📁 7
index.html [34.8 KB]
section.1.html [55.1 KB]
📁 📁 2
section.4.1.html [40.9 KB]
section.8.1.html [36.5 KB]
section.6.html [40.1 KB]
section.10.html [37.7 KB]
section.2.html [40.0 KB]
section.5.html [41.2 KB]
section.7.html [38.1 KB]
section.1.html [37.6 KB]
section.3.html [44.0 KB]
index.html [36.1 KB]
section.4.html [39.9 KB]
section.9.html [38.8 KB]
section.99.html [34.9 KB]
section.11.html [43.8 KB]
section.8.html [37.4 KB]
📁 📁 1
📁 📁 section.0.assets
image-20210706204933781.png [612.9 KB]
section.8.html [41.5 KB]
section.3.html [48.3 KB]
section.6.html [46.2 KB]
index.html [36.4 KB]
section.0.html [46.9 KB]
section.99.html [34.9 KB]
section.2.html [41.9 KB]
section.9.html [39.9 KB]
section.4.html [44.2 KB]
section.1.html [78.6 KB]
section.5.html [42.4 KB]
section.7.html [38.7 KB]
📁 📁 4
index.html [36.2 KB]
section.12.html [37.8 KB]
section.11.html [38.1 KB]
section.6.html [38.8 KB]
section.2.html [38.2 KB]
section.10.html [42.3 KB]
section.99.html [34.9 KB]
section.5.html [36.8 KB]
section.1.html [44.2 KB]
section.3.html [40.3 KB]
section.9.html [37.2 KB]
section.7.html [43.5 KB]
section.4.html [39.7 KB]
section.13.html [39.8 KB]
section.8.html [37.8 KB]
📁 📁 13
index.html [34.8 KB]
section.1.html [41.4 KB]
📁 📁 6
section.2.html [49.9 KB]
section.1.html [45.0 KB]
index.html [35.7 KB]
section.99.html [34.9 KB]
📁 📁 5
index.html [35.6 KB]
section.1.html [52.9 KB]
section.99.html [34.9 KB]
📁 📁 Images
20170109101127542.png [35.3 KB]
TIOBE-201805.JPG [47.1 KB]
pycharm.jpg [240.5 KB]
01-第10天-4.png [30.4 KB]
01-第1天-10.png [469.5 KB]
win.jpg [219.4 KB]
python模块.jpg [172.6 KB]
01-第5天-14.png [43.5 KB]
01-第2天-2.jpg [88.2 KB]
01-第1天-6.jpg [32.6 KB]
01-第5天-9.jpg [80.6 KB]
README-9.png [539.2 KB]
01-第5天-5.jpg [399.6 KB]
步骤4.jpg [309.6 KB]
01-第8天-2.png [27.6 KB]
容器.jpg [110.4 KB]
模块.png [102.7 KB]
01-第6天-4.png [374.1 KB]
p步骤5.jpg [83.0 KB]
2.png [438.2 KB]
01-第1天-20.png [120.3 KB]
下载.jpg [365.7 KB]
01-第1天-26.png [154.5 KB]
手翻书动画-2.gif [1.6 MB]
1.png [757.5 KB]
冯诺依曼体系结构.png [66.1 KB]
python使用场景.jpg [155.7 KB]
解释器2.jpg [385.8 KB]
01-第4天-12.gif [39.7 KB]
步骤3.jpg [379.6 KB]
id_ref.png [13.2 KB]
watermark.jpg [28.1 KB]
01-第1天-13.jpg [11.3 KB]
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01-第2天-3.jpg [22.8 KB]
reduce函数.bmp [2.2 MB]
01-第1天-7.png [50.8 KB]
中国.jpg [153.3 KB]
01-第1天-4.jpg [13.2 KB]
01-第10天-6.png [18.0 KB]
README-5.png [351.2 KB]
结果.jpg [89.8 KB]
README-10.png [198.1 KB]
01-第1天-20.1.png [29.4 KB]
01-第3天-7.gif [6.8 KB]
pycharm2.jpg [291.3 KB]
菜.png [299.4 KB]
01-第5天-7.png [114.8 KB]
python容器all.png [263.3 KB]
01-第5天-16.png [42.7 KB]
01-第1天-16.jpg [147.9 KB]
day01.png [125.6 KB]
QQ20170713-144621@2x.jpg [118.0 KB]
函数.jpg [211.9 KB]
01-第3天-6.jpg [12.0 KB]
README-8.png [342.6 KB]
引用.jpg [93.2 KB]
01-第5天-2.gif [89.2 KB]
01-第5天-4.png [199.9 KB]
01-第1天-17.png [107.1 KB]
01-第5天-18.png [48.3 KB]
01-第2天-7.jpg [396.2 KB]
01-第5天-8.jpg [73.3 KB]
01-第10天-2.png [19.9 KB]
01-第1天-12.gif [1.0 MB]
模块.jpg [90.0 KB]
TIOBE-201708.jpg [125.3 KB]
python基本语句.jpg [175.7 KB]
01-第2天-6.gif [377.5 KB]
01-第3天-5.jpg [18.9 KB]
步骤2.jpg [366.7 KB]
输出结果.jpg [82.8 KB]
p步骤3.jpg [161.6 KB]
language_index.png [56.1 KB]
手翻书动画-3.gif [1.1 MB]
01-第5天-10.jpg [36.7 KB]
01-第1天-11.jpg [32.8 KB]
README-7.png [371.9 KB]
01-第5天-3.png [25.5 KB]
变量.jpg [181.5 KB]
01-第2天-10.png [8.8 KB]
01-第2天-9.png [3.5 KB]
python文件.jpg [169.5 KB]
01-第10天-5.png [13.8 KB]
01-第1天-9.gif [5.9 KB]
01-第3天-11.png [40.9 KB]
输入.png [542.2 KB]
01-第6天-3.jpg [108.5 KB]
01-第1天-1.gif [3.3 MB]
TIOBE-20201.png [150.4 KB]
digui_jiecheng.png [43.4 KB]
01-第1天-18.png [78.1 KB]
机器.jpg [292.9 KB]
01-第5天-12.png [26.7 KB]
01-第2天-8.png [8.3 KB]
分支语句.jpg [81.7 KB]
解释器.jpg [476.9 KB]
README-3.png [406.9 KB]
python文件.png [120.9 KB]
01-第3天-2.gif [12.2 KB]
📁 📁 gitbook
📁 📁 fonts
📁 📁 fontawesome
FontAwesome.otf [73.4 KB]
fontawesome-webfont.svg [247.5 KB]
fontawesome-webfont.woff [81.8 KB]
fontawesome-webfont.eot [70.8 KB]
fontawesome-webfont.ttf [138.2 KB]
📁 📁 plugins
📁 📁 gitbook-plugin-toggle-chapters
toggle.css
toggle.js [687.0 B]
📁 📁 gitbook-plugin-splitter
splitter.css [503.0 B]
splitter.js [3.8 KB]
📁 📁 gitbook-plugin-emphasize
plugin.css [209.0 B]
📁 📁 gitbook-plugin-sharing
buttons.js [2.9 KB]
📁 📁 gitbook-plugin-fontsettings
buttons.js [3.9 KB]
website.css [8.4 KB]
📁 📁 gitbook-plugin-highlight
ebook.css [2.7 KB]
website.css [30.0 KB]
📁 📁 images
apple-touch-icon-precomposed-152.png [90.6 KB]
favicon.ico [4.2 KB]
style.css [38.3 KB]
app.js [741.5 KB]
index.html [34.6 KB]
📁 📁 day06
📁 📁 笔记
📁 📁 img
image-20220518104237145.png [88.2 KB]
image-20220518121028134.png [120.4 KB]
image-20220518102811720.png [108.1 KB]
image-20220518103918803.png [62.8 KB]
image-20220518163450949.png [404.6 KB]
image-20220518121154186.png [112.1 KB]
image-20220518174638211.png [228.6 KB]
day06笔记.md [28.5 KB]
📁 📁 作业
07-函数作业.md [4.5 KB]
📁 📁 代码
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
misc.xml [188.0 B]
workspace.xml [9.9 KB]
代码.iml [291.0 B]
.gitignore [50.0 B]
modules.xml [271.0 B]
15-形参-缺省参数.py [971.0 B]
01-函数的简单使用.py [1.5 KB]
14-形参-位置参数.py [434.0 B]
06-变量的作用域.py [1.6 KB]
18-组包和拆包.py [825.0 B]
04-函数的参数.py [1.7 KB]
07-global关键字.py [2.8 KB]
11-返回值加强.py [1.1 KB]
10-函数的参数和返回值传递流程.py [536.0 B]
17-形参-关键字不定长参数.py [2.0 KB]
08-函数定义中嵌套函数的调用.py [936.0 B]
20-可变数据类型和不可变数据类型.py [1.7 KB]
09-函数的执行流程.py [1.4 KB]
19-引用.py [2.9 KB]
16-形参-位置不定长参数.py [1.8 KB]
12-实参--位置参数.py [529.0 B]
13-实参--关键字参数赋值.py [1.8 KB]
03-函数的说明文档.py [1015.0 B]
05-函数的返回值.py [999.0 B]
02-定义函数的注意事项.py [707.0 B]
📁 📁 软件
📁 📁 02_Pycharm（必装）
📁 📁 2.windows
pycharm 安装教程.mp4 [36.7 MB]
pycharm-community-2020.1.exe [267.3 MB]
📁 📁 3.Mac OS
开启任何来源.txt [27.0 B]
pycharm-community-2020.1.dmg [360.2 MB]
pycharm安装教程_1.mp4 [8.2 MB]
1.pycharm安装教程.md [2.6 KB]
1.pycharm安装教程.pdf [2.1 MB]
📁 📁 01_Python解释器(必装)
📁 📁 3.Mac OS
python-3.8.2-macosx10.9.pkg [28.6 MB]
python解释器安装教程.mp4 [3.6 MB]
📁 📁 2.windows
python-3.8.2-amd64.exe [26.3 MB]
02_添加 Python 到环境变量.mp4 [104.1 MB]
01-Python 的安装.mp4 [74.2 MB]
1.python解释器安装教程.pdf [6.0 MB]
1.python解释器安装教程.md [2.2 KB]
📁 📁 03-文件同步软件
📁 📁 macOS
Resilio-Sync.dmg [24.8 MB]
📁 📁 windows
Resilio-Sync_x64.exe [33.8 MB]
📁 📁 04_typora及Xmind(必装)
📁 📁 typora
📁 📁 3.Mac OS
Typora.dmg [10.7 MB]
📁 📁 2.Windows
typora-setup-x64.exe [48.6 MB]
1.Typora 基本使用.md [1.5 KB]
1.Typora 基本使用.pdf [352.3 KB]
📁 📁 xmind
📁 📁 windows
安装前请先阅读.txt [19.0 B]
xmind-8-windows.exe [154.1 MB]
📁 📁 mac
xmind-8-macosx.dmg [170.1 MB]
📁 📁 day03
📁 📁 作业
03_循环作业.md [8.3 KB]
📁 📁 代码
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
misc.xml [188.0 B]
.gitignore [50.0 B]
代码.iml [291.0 B]
modules.xml [271.0 B]
workspace.xml [10.2 KB]
01-while的应用--1-100的累加和.py [1.9 KB]
02-while的应用-1-100的偶数累加和.py [841.0 B]
11-continue.py [1.1 KB]
15-猜数游戏.py [1.2 KB]
03-循环的嵌套.py [1.7 KB]
12-break和continue的注意事项.py [1.5 KB]
13-循环结构中的else.py [2.6 KB]
14-报数小游戏.py [1.2 KB]
08-for循环的应用--输出矩形.py [1.9 KB]
10-break.py [749.0 B]
09-for循环的应用--九九乘法表.py [2.2 KB]
06-for循环.py [1.1 KB]
04-循环嵌套的应用--输出矩形.py [2.3 KB]
07-for配合range函数使用.py [2.0 KB]
05-猜拳游戏优化.py [1.9 KB]
📁 📁 笔记
📁 📁 img
image-20220514120738331.png [99.0 KB]
image-20220514170939957.png [157.7 KB]
image-20220514171102340.png [198.8 KB]
day03笔记.md [24.1 KB]
📁 📁 day08
📁 📁 作业
📁 📁 代码
📁 📁 my_package
📁 📁 __pycache__
__init__.cpython-38.pyc [183.0 B]
my_module_02.cpython-38.pyc [560.0 B]
my_module__all__.cpython-38.pyc [542.0 B]
my_module_03.cpython-38.pyc [481.0 B]
my_module__all__.py [188.0 B]
my_module_02.py [396.0 B]
__init__.py
my_module_03.py [134.0 B]
📁 📁 __pycache__
my_module__all__.cpython-38.pyc [531.0 B]
my_module_01.cpython-38.pyc [470.0 B]
📁 📁 my_dir
__init__.py
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
misc.xml [188.0 B]
代码.iml [291.0 B]
workspace.xml [11.0 KB]
.gitignore [50.0 B]
modules.xml [271.0 B]
📁 📁 py
[黑马]python基础班-1.txt
[黑马]python基础班-5.txt
[黑马]python基础班-3.txt
[黑马]python基础班-4.txt
[黑马]python基础班-2.txt
09-模块的导入方式.py [1.4 KB]
17-学生管理系统抽取函数.py [2.9 KB]
05-异常中的else.py [719.0 B]
03-捕获指定类型的异常.py [2.0 KB]
my_module__all__.py [188.0 B]
02-异常捕获体验.py [1.1 KB]
18-学生管理系统--添加学员.py [3.1 KB]
01-常见异常介绍.py [450.0 B]
04-捕获异常描述信息.py [1.2 KB]
19-学生管理系统--删除学员.py [3.5 KB]
0_0_chuanzhi.py [58.0 B]
14-测试代码的书写位置.py [713.0 B]
00-作业讲解.py [980.0 B]
10-给模块或功能起别名.py [1023.0 B]
22-学生管理系统--退出系统.py [5.2 KB]
22-学生管理系统--展示所有学员信息.py [5.0 KB]
15-学生管理系统需求分析.py [777.0 B]
1.txt [63.0 B]
20-学生管理系统--修改学员.py [4.2 KB]
my_module_01.py [134.0 B]
12-__all__的使用.py [1.2 KB]
11-自定义模块.py [778.0 B]
23-PEP8语法规范.py [7.0 B]
16-学生管理系统框架搭建.py [1.0 KB]
06-异常中的finally.py [1.6 KB]
07-异常捕获练习.py [622.0 B]
08-异常穿透.py [632.0 B]
13-包的使用.py [1.1 KB]
21-学生管理系统--查询学员信息.py [4.8 KB]
📁 📁 笔记
📁 📁 img
image-20220521144816423.png [153.4 KB]
image-20220521144746376.png [146.5 KB]
day08笔记.md [33.8 KB]
Python基础班重点内容.md [2.0 KB]
📁 📁 day04
📁 📁 代码
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
misc.xml [188.0 B]
代码.iml [291.0 B]
workspace.xml [9.9 KB]
.gitignore [50.0 B]
modules.xml [271.0 B]
01-字符串的定义.py [1.3 KB]
15-列表的操作--查.py [1.3 KB]
09-字符串的应用.py [1.8 KB]
03-字符串的下标.py [1.7 KB]
14-列表的操作-增.py [1.8 KB]
11-字符串的方法补充2.py [1.6 KB]
07-字符串的替换方法.py [1.0 KB]
04-字符串切片.py [2.3 KB]
02-多种字符串定义方式嵌套使用.py [1.1 KB]
13-列表的遍历.py [812.0 B]
05-字符串切片的省略.py [1.8 KB]
12-列表的定义.py [1.1 KB]
08-字符串的拆分方法.py [1.5 KB]
00-作业讲解.py [1.2 KB]
10-字符串的方法补充.py [1.5 KB]
06-字符串的查找方法.py [3.3 KB]
📁 📁 作业
04_字符串作业.md [4.0 KB]
05_列表作业.md [2.7 KB]
📁 📁 笔记
📁 📁 img
image-20220515113548579.png [166.0 KB]
📁 📁 images
1691033937521.png [109.6 KB]
1691034134381.png [295.4 KB]
day04笔记.md [25.0 KB]
📁 📁 day01
📁 📁 作业
01-Python入门作业.md [5.9 KB]
📁 📁 代码
📁 📁 python_demo
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
workspace.xml [10.2 KB]
.gitignore [50.0 B]
misc.xml [188.0 B]
python_demo.iml [291.0 B]
modules.xml [281.0 B]
03-变量.py [1022.0 B]
08-输出函数详解.py [856.0 B]
04-变量的类型.py [1.6 KB]
10-数据类型转换.py [1.6 KB]
01-第一个python程序.py [23.0 B]
02-注释.py [1.1 KB]
09-python的输入函数.py [1.7 KB]
05-标识符和关键字.py [1.7 KB]
07-处理格式化输出中的精度问题.py [1.1 KB]
06-输出.py [2.7 KB]
📁 📁 笔记
📁 📁 img
image-20220511111509373.png [316.4 KB]
image-20220511103021811.png [176.9 KB]
image-20220511112920381.png [211.2 KB]
image-20220511145717837.png [178.1 KB]
image-20220511113722854.png [332.4 KB]
image-20220511111348666.png [247.7 KB]
image-20220511113448520.png [287.3 KB]
image-20220511111828749.png [397.1 KB]
image-20220511104302487.png [633.3 KB]
image-20220511115328840.png [524.8 KB]
image-20220511113254086.png [559.2 KB]
image-20220511115503255.png [519.6 KB]
image-20220511150203130.png [705.3 KB]
image-20220511115057232.png [629.4 KB]
image-20220511115200582.png [220.8 KB]
image-20220511121552800.png [251.5 KB]
image-20220511093727668.png [150.5 KB]
image-20220511145927820.png [587.7 KB]
image-20220511145803802.png [257.9 KB]
📁 📁 images
1690426810377.png [24.7 KB]
1690430246576.png [134.7 KB]
1690426732133.png [70.3 KB]
day01笔记.md [23.4 KB]
hello world.py [23.0 B]
📁 📁 day02
📁 📁 代码
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
misc.xml [188.0 B]
workspace.xml [11.0 KB]
modules.xml [271.0 B]
.gitignore [50.0 B]
代码.iml [291.0 B]
12-分支语句的嵌套.py [1.7 KB]
16-while循环详解.py [1.4 KB]
01-f-string字符串.py [2.1 KB]
03-算数运算符.py [1.6 KB]
09-单条件分支语句.py [838.0 B]
04-赋值运算符.py [1.6 KB]
07-逻辑运算符.py [916.0 B]
14-三目运算符.py [849.0 B]
08-三大流程语句.py [507.0 B]
11-多条件分支语句.py [3.4 KB]
13-猜拳游戏.py [1.0 KB]
05-比较运算符.py [1.1 KB]
10-对立条件分支语句.py [982.0 B]
00-作业讲解.py [53.0 B]
02-变量的数据类型补充.py [2.5 KB]
15-循环语句的体验.py [529.0 B]
06-字符串之间的大小比较.py [1.7 KB]
📁 📁 笔记
📁 📁 img
image-20220512161710887.png [813.8 KB]
image-20220512111244281.png [83.3 KB]
image-20220512150659892.png [256.8 KB]
image-20220512182311655.png [74.3 KB]
image-20220512155046174.png [422.8 KB]
image-20220512152613300.png [702.8 KB]
image-20220512182230498.png [78.0 KB]
day02笔记.md [24.6 KB]
📁 📁 作业
02_分支语句作业.md [5.1 KB]
作业提交秘钥.txt [33.0 B]
📁 📁 阶段8-AI医疗项目实战
📁 📁 day06
📁 📁 2 笔记
📁 📁 day06笔记.assets
image-20220629165015125.png [191.5 KB]
image-20220629154047902.png [301.5 KB]
image-20220629154522564.png [307.2 KB]
image-20220629154313536.png [171.6 KB]
day06笔记.md [19.6 KB]
📁 📁 3 代码
ai_doctor_code.zip [4.1 MB]
📁 📁 4 其他
📁 📁 注册微信公众号.assets
image-20220627011307757.png [264.0 KB]
image-20220627011207023.png [59.0 KB]
image-20220627011534215.png [169.7 KB]
image-20220627011643647.png [159.8 KB]
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📁 📁 img
2.png [118.0 KB]
4.png [264.1 KB]
6.png [87.3 KB]
1.png [59.0 KB]
5.png [91.9 KB]
7.png [496.2 KB]
3.png [334.6 KB]
train_data.csv [3.3 MB]
花生壳注册.md [701.0 B]
BiLSTM+CRF.xmind [126.6 KB]
注册微信公众号.md [676.0 B]
📁 📁 模型部署
📁 📁 2 课件
模型部署.zip [1.0 MB]
📁 📁 3 练习
模型部署练习.zip [46.7 MB]
📁 📁 day02
📁 📁 3 代码
📁 📁 doctor_offlinev3
📁 📁 review_model
predict.py [2.1 KB]
rnn_model.py [1.2 KB]
bert_chinese_encode.py [1.1 KB]
train_data.csv [214.7 KB]
train.py [5.8 KB]
config.py [95.0 B]
wirte_to_neo4j.py [2.1 KB]
📁 📁 doctor_offline_old
📁 📁 review_model
bert_chinese_encode.py [1.3 KB]
RNN_MODEL.py [1.4 KB]
acc.png [31.9 KB]
BERT_RNN.pth [451.8 KB]
train_data.csv [214.7 KB]
loss.png [33.0 KB]
train.py [6.5 KB]
wirte_to_neo4j.py [2.3 KB]
📁 📁 doctor_offlinev1
📁 📁 review_model
train.py [1.9 KB]
train_data.csv [214.7 KB]
rnn_model.py [1.2 KB]
bert_chinese_encode.py [1.1 KB]
config.py [95.0 B]
wirte_to_neo4j.py [2.1 KB]
📁 📁 doctor_offlinev2
📁 📁 review_model
train_data.csv [214.7 KB]
train.py [5.8 KB]
bert_chinese_encode.py [1.1 KB]
rnn_model.py [1.2 KB]
config.py [95.0 B]
wirte_to_neo4j.py [2.1 KB]
📁 📁 4 其他
structured_unstructured_data.zip [7.1 MB]
作业.md [251.0 B]
movie_full_info.txt [27.8 KB]
📁 📁 2 笔记
📁 📁 day02笔记.assets
image-20220809113113340.png [15.3 KB]
RNN内部结构图.png [45.9 KB]
image-20220621085329846.png [36.1 KB]
image-20221103113524415.png [108.5 KB]
image-20220809112948941.png [159.8 KB]
image-20220621090530688.png [126.9 KB]
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day02笔记.md [18.0 KB]
📁 📁 day01
📁 📁 2 笔记
📁 📁 day01笔记.assets
标记属性图模型-16599455086212.png [68.4 KB]
AI医生架构.svg [47.5 KB]
image-20220808103446088.png [7.4 KB]
image-20220808115934046.png [11.4 KB]
image-20220808115022629.png [11.8 KB]
标记属性图模型.png [68.4 KB]
image-20220808115817940.png [98.5 KB]
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📁 📁 assets
image-20230624101700860.png [110.0 KB]
image-20230624152053288.png [163.4 KB]
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image-20230624101737968.png [56.8 KB]
image-20230624113059513.png [9.5 KB]
image-20230624112948286.png [60.9 KB]
day01笔记.md [20.2 KB]
📁 📁 4 其他
movie_full_info.txt [27.8 KB]
作业.md [3.5 KB]
📁 📁 3 代码
test_transcation.py [952.0 B]
test_flask.py [383.0 B]
neo4j.conf [15.6 KB]
test_redis.py [770.0 B]
test_neo4j.py [582.0 B]
main.py [544.0 B]
supervisord.conf [10.4 KB]
baidu_unit.py [3.3 KB]
learning.py [253.0 B]
📁 📁 day05
📁 📁 2 笔记
📁 📁 assets
image-20230630175022599.png [157.3 KB]
day05笔记.md [17.3 KB]
📁 📁 4 其他
📁 📁 注册微信公众号.assets
image-20220627011643647.png [159.8 KB]
image-20220627011326838.png [201.5 KB]
image-20220627011143108.png [149.1 KB]
image-20220627011307757.png [264.0 KB]
image-20220627011534215.png [169.7 KB]
image-20220627011207023.png [59.0 KB]
image-20220627011221729.png [248.1 KB]
image-20220627012030566.png [316.4 KB]
注册微信公众号.md [612.0 B]
📁 📁 3 代码
📁 📁 ner_model参考代码
📁 📁 ner_data
train.txt [262.5 KB]
char_to_id.json [380.8 KB]
validate.txt [224.8 KB]
📁 📁 model
bilstm_crf.py [11.3 KB]
train.py [3.8 KB]
entity_extract.py [2.7 KB]
evaluate.py [6.1 KB]
build_vocab.py [560.0 B]
load_corpus.py [952.0 B]
encode_label.py [1.2 KB]
ner_model.zip [143.0 KB]
📁 📁 AI医生课件
📁 📁 search
search_index.json [518.4 KB]
📁 📁 assets
📁 📁 stylesheets
main.cd566b2a.min.css.map [44.2 KB]
main.cd566b2a.min.css [131.4 KB]
palette.e6a45f82.min.css.map [3.1 KB]
palette.e6a45f82.min.css [10.4 KB]
📁 📁 images
logo.svg [9.2 KB]
favicon.png [1.8 KB]
📁 📁 javascripts
📁 📁 workers
search.22074ed6.min.js.map [165.1 KB]
search.22074ed6.min.js [35.4 KB]
📁 📁 lunr
📁 📁 min
lunr.sv.min.js [4.4 KB]
lunr.no.min.js [4.6 KB]
lunr.de.min.js [6.0 KB]
lunr.pt.min.js [9.9 KB]
lunr.fi.min.js [9.1 KB]
lunr.tr.min.js [14.7 KB]
lunr.ja.min.js [2.3 KB]
lunr.da.min.js [4.5 KB]
lunr.th.min.js [1.0 KB]
lunr.multi.min.js [817.0 B]
lunr.fr.min.js [10.4 KB]
lunr.ar.min.js [16.7 KB]
lunr.du.min.js [6.1 KB]
lunr.vi.min.js [784.0 B]
lunr.ru.min.js [10.1 KB]
lunr.hi.min.js [3.3 KB]
lunr.ro.min.js [10.7 KB]
lunr.jp.min.js [36.0 B]
lunr.stemmer.support.min.js [3.6 KB]
lunr.zh.min.js [2.0 KB]
lunr.hu.min.js [9.2 KB]
lunr.it.min.js [11.0 KB]
lunr.nl.min.js [5.9 KB]
lunr.es.min.js [11.2 KB]
tinyseg.js [22.3 KB]
wordcut.js [661.6 KB]
bundle.1514a9a0.min.js.map [480.3 KB]
bundle.1514a9a0.min.js [102.2 KB]
📁 📁 img
12.jpeg [157.5 KB]
20190114.png [129.1 KB]
35.jpeg [78.0 KB]
7.jpeg [105.5 KB]
bilstm_crf_train_F1.png [39.8 KB]
WechatIMG2.jpeg [34.2 KB]
image-20220602185952734.png [213.5 KB]
loss.png [23.5 KB]
ner_demo03.png [10.9 KB]
知识图谱技术链.png [205.2 KB]
neo4j内存管理.png [75.8 KB]
26.jpeg [91.3 KB]
RNN公式图.png [1.4 KB]
6_1_NER_demo_3.png [38.0 KB]
5.jpeg [104.7 KB]
npz.png [2.0 KB]
doctor写入效果.png [98.8 KB]
alibaba2.png [362.4 KB]
emission_matrix.png [95.9 KB]
doctorAI_offline2.png [34.5 KB]
32.jpeg [50.4 KB]
picture6_0512.png [56.1 KB]
alibaba1.png [293.3 KB]
northwind数据图示.jpg [88.9 KB]
18.jpeg [96.5 KB]
标记属性图模型.png [68.4 KB]
19.jpeg [93.0 KB]
ner_demo04.png [11.3 KB]
tanh激活函数.gif [51.6 KB]
27.jpeg [124.7 KB]
logo.png [7.7 KB]
neo4j可视化.jpg [353.8 KB]
bilstm_crf_train_Acc.png [45.4 KB]
acc.png [21.9 KB]
读语句案例2.jpg [182.2 KB]
weixin1.png [224.4 KB]
13.jpeg [154.5 KB]
数据驱动与知识驱动对比.png [265.3 KB]
1.jpeg [111.2 KB]
插入效果.png [105.0 KB]
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添加约束失败.jpg [77.1 KB]
读语句案例1.jpg [163.1 KB]
图数据库统计.jpg [418.9 KB]
8.jpeg [147.3 KB]
picture4_0512.png [109.8 KB]
11.jpeg [157.8 KB]
bilstm_crf_train_Loss.png [27.3 KB]
结构解释图.png [18.6 KB]
transition_matrix.png [14.4 KB]
24.jpeg [151.5 KB]
10.png [63.7 KB]
picture2_0512.png [69.0 KB]
4.jpeg [93.0 KB]
6_1_NER_demo_1.png [17.5 KB]
6_3_biLSTM_CRF_network.jpg [76.7 KB]
redis.png [33.5 KB]
机器人类分辨猫.jpg [282.4 KB]
2.jpeg [146.6 KB]
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image-20220602185800109.png [140.9 KB]
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20.jpeg [91.6 KB]
RNN结构过程图.gif [54.4 KB]
picture1_0512.png [99.2 KB]
23.jpeg [142.6 KB]
doctorAI_luoji.png [84.4 KB]
Flask_1.png [23.1 KB]
14.jpeg [157.6 KB]
transition.jpg [22.7 KB]
bilstm_crf_train_Recall.png [42.0 KB]
6.jpeg [99.3 KB]
picture3_0512.png [255.8 KB]
alibaba5.png [377.4 KB]
WechatIMG1.jpeg [55.7 KB]
核心类型映射过程.png [211.7 KB]
alibaba4.png [350.0 KB]
rnn_loss.png [38.0 KB]
alibaba3.png [367.9 KB]
20190114_1.jpeg [63.9 KB]
30.jpeg [119.0 KB]
bilstm.jpg [98.9 KB]
Supervisor.png [49.2 KB]
人物关系图.jpg [42.8 KB]
doctorAI_offline1.png [79.8 KB]
21.jpeg [84.5 KB]
RNN内部结构图.png [45.9 KB]
RDF与图数据库.jpg [109.1 KB]
Flask.png [54.4 KB]
doctorAI.png [79.8 KB]
image-20220602190049465.png [64.9 KB]
图数据库特性.jpg [621.8 KB]
25.jpeg [129.6 KB]
6_1_NER_demo_2.png [15.7 KB]
gunicorn.png [18.3 KB]
3.jpeg [104.9 KB]
36.jpeg [28.3 KB]
neo4j.png [13.1 KB]
3.html [67.7 KB]
4.html [28.7 KB]
6.html [149.7 KB]
index.html [8.6 KB]
5.html [73.5 KB]
8.html [81.1 KB]
sitemap.xml [1.4 KB]
2.html [39.8 KB]
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1.html [14.1 KB]
404.html [7.5 KB]
7.html [51.9 KB]
📁 📁 day07
📁 📁 3 代码
结构化&非结构化数据.zip [6.5 MB]
ai_doctor_code.zip [12.6 MB]
📁 📁 2 笔记
📁 📁 day07笔记.assets
image-20220701155557607.png [450.1 KB]
image-20220701174538531.png [38.0 KB]
image-20220630101443885.png [163.0 KB]
image-20220701152847701.png [39.5 KB]
image-20220630195408453.png [343.7 KB]
AI医生架构.svg [47.5 KB]
day07笔记.md [33.0 KB]
supervisord.conf [10.4 KB]
📁 📁 day04
📁 📁 2 笔记
📁 📁 assets
image-20220812172849637.png [106.6 KB]
image-20220812172821139.png [256.6 KB]
📁 📁 day04笔记.assets
image-20220814114458904-16604493205991.png [25.2 KB]
image-20220814114458904.png [25.2 KB]
image-20220626105419340.png [13.0 KB]
image-20220626095218711.png [286.2 KB]
image-20220626103234355.png [43.1 KB]
image-20220626114622483.png [7.7 KB]
image-20220626103157068.png [9.6 KB]
image-20220814115528941.png [31.9 KB]
image-20220626103208583.png [12.1 KB]
day04笔记.md [16.0 KB]
📁 📁 4 其他
BiLSTM+CRF实现命名实体识别.pdf [895.9 KB]
📁 📁 3 代码
📁 📁 ner_model
📁 📁 ner_data
validate.txt [224.8 KB]
train.txt [262.5 KB]
char_to_id.json [380.8 KB]
bilstm_crf_参考代码.py [9.1 KB]
bilstm_crf.py [6.6 KB]
📁 📁 day03
📁 📁 2 笔记
📁 📁 assets
维特比算法分词应用.svg [19.6 KB]
image-20230626184029704.png [188.8 KB]
day03笔记.md [17.2 KB]
📁 📁 4 其他
📁 📁 统计语言模型.assets
image-20220811160757026.png [266.0 KB]
image-20220811160756835.png [266.0 KB]
📁 📁 ner_data
train.txt [262.5 KB]
valid.csv [111.3 KB]
validate.txt [224.8 KB]
train.csv [129.0 KB]
📁 📁 img
局部马尔可夫性.svg [11.4 KB]
无向图的团与最大团.svg [5.9 KB]
X和Y有相同图结构的线性链条件随机场.svg [12.7 KB]
维特比算法流程图.svg [19.7 KB]
状态序列观测序列.svg [7.4 KB]
求最优路径.svg [18.0 KB]
全局马尔可夫性.svg [10.8 KB]
crf概念导图.html [135.9 KB]
线性链条件随机场.svg [9.1 KB]
状态序列观测序列(1).svg [7.4 KB]
统计语言模型.md [44.7 KB]
BiLSTM+CRF实现命名实体识别.pdf [895.9 KB]
Neo4J实战教程.md [6.0 KB]
HMMTrainSet.txt [7.4 MB]
test2_org.txt [1.1 KB]
test1_org.txt [2.7 KB]
day01-day02 cypher作业答案.txt [878.0 B]
test1_cut.txt [3.9 KB]
📁 📁 3 代码
📁 📁 ner_model
📁 📁 ner_data
validate.txt [224.8 KB]
train.txt [262.5 KB]
char_to_id.json [380.8 KB]
bilstm_crf.py [2.4 KB]
📁 📁 HMM
hmm.py [2.0 KB]
hmm_cut.py [7.4 KB]
test1_cut.txt [3.9 KB]
test1_org.txt [2.7 KB]
ai虚拟机快照.png [77.1 KB]
AI医生项目文档参考.zip [190.1 KB]
AI医生架构.svg [47.5 KB]
📁 📁 阶段5-金融风控项目与数据挖掘
📁 📁 day07
📁 📁 数据
BostonHousing.csv [34.9 KB]
appli_reject.txt [8.1 MB]
📁 📁 代码
12_拒绝推断.ipynb [73.2 KB]
14_GBDT特征交叉.ipynb [175.9 KB]
13_模型可解释性.ipynb [618.3 KB]
📁 📁 课件
📁 📁 特征交叉
📁 📁 assets
image-20220519062201606.png [20.6 KB]
1473228-20180917183111311-2021770645.png [45.7 KB]
image-20220519055949947.png [21.0 KB]
image-20220519060357058.png [21.0 KB]
树模型特征衍生.md [9.8 KB]
📁 📁 笔记
📁 📁 assets
image-20220519041747951.png [24.6 KB]
shap12.png [26.0 KB]
shap13.png [34.1 KB]
shap11.png [19.8 KB]
image-20220519043413900.png [19.5 KB]
image-20220519041922701.png [4.4 KB]
shap14.png [19.5 KB]
shap10.png [30.8 KB]
day06.xmind [185.8 KB]
笔记.md [17.1 KB]
📁 📁 day05
📁 📁 笔记
📁 📁 assets
image-20230928113321647.png [33.8 KB]
image-20230928112426202.png [18.2 KB]
笔记.md [11.8 KB]
特征工程.xmind [51.3 KB]
评分卡.xmind [96.9 KB]
📁 📁 代码
08_LightGBM评分卡.ipynb [44.7 KB]
07_lightGBM的API.ipynb [166.5 KB]
06_Histogram-based_Gradient_Boosting.ipynb [197.9 KB]
📁 📁 day01
📁 📁 数据
loan.sql [2.2 MB]
业务数据.xlsx [404.3 KB]
📁 📁 笔记
📁 📁 assets
image-20230923155251394.png [38.4 KB]
fk3.png [39.8 KB]
image-20230923155029842.png [63.5 KB]
fk4.png [31.4 KB]
fk2.png [39.4 KB]
image-20230923155133299.png [85.1 KB]
笔记.md [26.5 KB]
📁 📁 课件
📁 📁 img
loan6.png [16.7 KB]
loan7.png [18.2 KB]
loan4.png [14.5 KB]
loan5.png [18.1 KB]
金融风控项目.pptx [5.7 MB]
风控报表_sql.md [14.2 KB]
📁 📁 代码
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
Project_Default.xml [431.0 B]
📁 📁 dataSources
📁 📁 f54f9415-b508-4d79-a8fc-0f2518dd6a66
📁 📁 storage_v2
📁 📁 _src_
📁 📁 schema
…（已达最大深度 10 层，子目录未展开）
f54f9415-b508-4d79-a8fc-0f2518dd6a66.xml [150.0 KB]
.gitignore [184.0 B]
workspace.xml [3.3 KB]
deployment.xml [636.0 B]
sqldialects.xml [174.0 B]
dataSources.local.xml [1.1 KB]
dataSources.xml [530.0 B]
modules.xml [271.0 B]
代码.iml [291.0 B]
misc.xml [210.0 B]
📁 📁 data
Bcard.txt [18.9 MB]
rule_data.xlsx [8.5 MB]
scorecard.txt [18.2 MB]
germancredit.csv [261.3 KB]
业务数据.xlsx [404.3 KB]
appli_reject.txt [8.1 MB]
train_woe.pkl [131.4 MB]
BostonHousing.csv [34.9 KB]
textdata.xlsx [10.5 KB]
03_特征构造.ipynb [2.6 MB]
12_拒绝推断.ipynb [73.2 KB]
02_业务规则挖掘.ipynb [175.4 KB]
dt.dot [611.0 B]
06_Histogram-based_Gradient_Boosting.ipynb [243.9 KB]
14_GBDT特征交叉.ipynb [175.9 KB]
08_LightGBM评分卡.ipynb [44.7 KB]
05_逻辑回归评分卡.ipynb [126.8 KB]
render.html [11.3 KB]
09_整体流程梳理.ipynb [762.2 KB]
10_样本不均衡问题处理.ipynb [45.3 KB]
01_业务数据处理案例.ipynb [56.6 KB]
04_特征筛选.ipynb [41.9 KB]
11_异常点检测.ipynb [69.3 KB]
07_lightGBM的API.ipynb [175.7 KB]
13_模型可解释性.ipynb [660.3 KB]
📁 📁 实战
📁 📁 软件
TortoiseGit-2.13.0.1-64bit.msi [20.2 MB]
Git-2.39.1-64-bit.exe [50.5 MB]
TortoiseGit-LanguagePack-2.13.0.0-64bit-zh_CN.msi [4.2 MB]
📁 📁 项目文档
风控项目文档.pdf [1.3 MB]
📁 📁 实战文档
📁 📁 人才流失预测
📁 📁 .idea
📁 📁 inspectionProfiles
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workspace.xml [2.0 KB]
deployment.xml [636.0 B]
modules.xml [471.0 B]
misc.xml [339.0 B]
人才流失预测.iml [413.0 B]
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人才流失模型-实战.ipynb [271.6 KB]
test.csv [47.0 KB]
train.csv [149.3 KB]
test2.csv [47.7 KB]
📁 📁 天猫复购预测
📁 📁 data_format1
user_info_format1.csv [4.3 MB]
train_format1.csv [3.4 MB]
test_format1.csv [3.1 MB]
user_log_format1.csv [1.8 GB]
📁 📁 .idea
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modules.xml [295.0 B]
train_v1.csv [9.5 MB]
data_format1.zip [360.2 MB]
天猫复购预测-实战.ipynb [15.0 KB]
user_log_format1.pkl [995.2 MB]
data_format2.zip [353.6 MB]
test_v1.csv [9.3 MB]
📁 📁 风控实战
📁 📁 img
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📁 📁 assets
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home-credit-default-risk (1).zip [688.2 MB]
金融风控实战.md [41.1 KB]
笔记.md [1.5 KB]
📁 📁 day04
📁 📁 代码
05_逻辑回归评分卡.ipynb [127.4 KB]
04_特征筛选.ipynb [41.9 KB]
📁 📁 笔记
📁 📁 assets
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r2_3.png [54.0 KB]
image-20230927160521224.png [28.7 KB]
r2_2.png [51.2 KB]
特征工程.xmind [212.3 KB]
笔记.md [16.7 KB]
📁 📁 课件
金融风控项目 - 03.pptx [7.2 MB]
📁 📁 数据
Bcard.txt [18.9 MB]
train_woe.pkl [131.4 MB]
01_Histogram-based_Gradient_Boosting.ipynb [197.9 KB]
📁 📁 day03
📁 📁 笔记
📁 📁 assets
image-20230926154114933.png [42.2 KB]
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image-20230926144332412.png [39.4 KB]
image-20230926180625431.png [29.3 KB]
风控建模概述.xmind [233.1 KB]
笔记.md [28.5 KB]
📁 📁 数据
germancredit.csv [261.3 KB]
textdata.xlsx [10.5 KB]
📁 📁 课件
金融风控项目 - 02.pptx [4.6 MB]
📁 📁 代码
03_特征构造.ipynb [2.6 MB]
📁 📁 day02
📁 📁 笔记
📁 📁 assets
笔记.md [72.8 KB]
金融风控业务概述.xmind [167.4 KB]
📁 📁 代码
📁 📁 data
rule_data.xlsx [8.5 MB]
📁 📁 .idea
workspace.xml [1.2 KB]
modules.xml [271.0 B]
代码.iml [291.0 B]
misc.xml [324.0 B]
01_业务规则.ipynb [230.5 KB]
📁 📁 数据
vintage.xlsx [16.2 KB]
rule_data.xlsx [8.5 MB]
📁 📁 软件
windows_10_cmake_Release_graphviz-install-2.50.0-win64.exe [4.5 MB]
📁 📁 day06
📁 📁 笔记
📁 📁 assets
image-20220517012341858.png [20.1 KB]
评分卡.xmind [202.3 KB]
笔记.md [474.0 KB]
📁 📁 数据
scorecard.txt [18.2 MB]
Bcard.txt [18.9 MB]
📁 📁 代码
10_样本不均衡问题处理.ipynb [45.3 KB]
11_异常点检测.ipynb [17.0 KB]
09_整体流程梳理.ipynb [761.7 KB]
📁 📁 课件
金融风控项目 - 04.pptx [2.7 MB]
📁 📁 阶段2-Python编程进阶
📁 📁 day10-复习回顾
📁 📁 06-今日总结
📁 📁 01-讲义
08_3数据结构与算法_链表.pdf [532.1 KB]
04_闭包和装饰器.pdf [437.7 KB]
06_2-线程.pdf [802.6 KB]
08_6数据结构与算法_二叉树.pdf [2.0 MB]
08_1数据结构与算法_概念时间复杂度.pdf [2.0 MB]
05_网络编程.pdf [2.9 MB]
01_Python面向对象基础.pdf [2.8 MB]
08_1数据结构与算法_数据结构 .pdf [968.0 KB]
08_5数据结构与算法_二分查找.pdf [558.8 KB]
06_1-进程.pdf [985.0 KB]
07_1Python正则表达式.pdf [582.7 KB]
08_2数据结构与算法_概念数据结构 .pdf [951.0 KB]
07_2Python其他高级语法.pdf [675.4 KB]
08_4数据结构与算法_排序.pdf [2.0 MB]
03_浅拷贝和深拷贝.pdf [784.8 KB]
02_Python面向对象高级.pdf [1.1 MB]
03_Python面向对象综合案例.pdf [550.0 KB]
📁 📁 05-作业
📁 📁 03-代码
📁 📁 课堂代码
dm03_装饰器.py [844.0 B]
dm13_客户端程序.py [1.4 KB]
zz32_服务器粘包.py [1.7 KB]
zz21_客户端粘包问题.py [1.8 KB]
zz22_服务器粘包问题.py [1.4 KB]
dm14_服务器支持多个客户端.py [1.6 KB]
dm01_多继承调用顺序.py [658.0 B]
zz31_客户端粘包.py [1.2 KB]
dm02-练习1警察通过各种警犬工作.py [912.0 B]
📁 📁 02-笔记
📁 📁 day07-正则表达式和时间复杂度
📁 📁 01-讲义
08_1数据结构与算法_数据结构 .pdf [968.0 KB]
07_1Python正则表达式.pdf [507.9 KB]
07_数据结构与算法_时间复杂度.pdf [2.0 MB]
📁 📁 03-代码
📁 📁 时间复杂度样例
zz03_链表.py [4.9 KB]
zz02_算法改进.py [354.0 B]
zz01_穷举法.py [392.0 B]
📁 📁 正则表达样例
zz05_匹配分组.py [3.3 KB]
zz06-练习.py [569.0 B]
zz04_匹配开头和结尾.py [1.6 KB]
zz01_re介绍.py [3.9 KB]
zz02_匹配单个字符.py [2.3 KB]
zz03_匹配多个字符.py [2.0 KB]
📁 📁 06-今日总结
📁 📁 02-笔记
📁 📁 08-数据结构与算法
📁 📁 images
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数据结构与算法.md [26.3 KB]
📁 📁 07-python高级语法笔记
📁 📁 images
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📁 📁 media
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Python高级语法与正则表达式.md [23.3 KB]
07-python高级语法笔记.zip [3.4 MB]
08-数据结构与算法.zip [7.8 MB]
📁 📁 05-作业
正则表达式算法复杂度-作业.md [236.0 B]
📁 📁 day03-学生管理系统
📁 📁 01-讲义
03_Python面向对象综合案例.pdf [549.9 KB]
03_浅拷贝和深拷贝.pdf [785.0 KB]
📁 📁 02-笔记
📁 📁 03-笔记
📁 📁 images
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回调函数.png [636.5 KB]
学员管理系统(面向对象).md [14.1 KB]
03-笔记.zip [6.8 MB]
📁 📁 03-代码
📁 📁 课堂代码02
📁 📁 __pycache__
dm01_student.cpython-38.pyc [1007.0 B]
dm03_回调函数.py [652.0 B]
dm01_student.py [867.0 B]
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📁 📁 样例
📁 📁 __pycache__
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zz01_student.cpython-38.pyc [920.0 B]
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mystudent.txt [264.0 B]
mystudent.data
zz04_回调函数.py [980.0 B]
zz03_main.py [227.0 B]
📁 📁 课堂代码01
dm02_studentcms.py [3.8 KB]
dm01_student.py [867.0 B]
📁 📁 05-作业
学员管理系统(面向对象)-作业.md [584.0 B]
📁 📁 06-今日总结
📁 📁 day02-面向对象高级
📁 📁 03-代码
📁 📁 样例
zz08_拓展.py [2.2 KB]
zz15_1对象属性.py [504.0 B]
zz15_2类属性.py [505.0 B]
zz04_多继承.py [1.6 KB]
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zz09_多层继承.py [1.3 KB]
zz13-空调抽象类.py [948.0 B]
zz07_super调用父类同名方法和属性.py [1.1 KB]
zz14-练习1警察通过各种警犬工作.py [871.0 B]
zz01_类定义三种方法.py [418.0 B]
zz15_4类静态方法.py [635.0 B]
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zz15_3类方法.py [632.0 B]
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zz06_子类调用父类同名方法和属性.py [1.4 KB]
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zz02_继承语法.py [688.0 B]
zz12-英雄战机多态.py [2.0 KB]
📁 📁 课堂
dm04_多继承.py [1.2 KB]
dm12_战斗多态.py [1.6 KB]
dm06_子类显示调用父类的属性和方法.py [1.4 KB]
dm15_2类属性.py [568.0 B]
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dm09_子类为啥不能继承了.py [606.0 B]
dm15_1属性.py [217.0 B]
dm16_类方法.py [278.0 B]
dm10_父类中有私有属性.py [1.6 KB]
dm07_使用super方法自动查找父类-调用父类方法.py [675.0 B]
dm08_使用super方法-两个父类.py [1.2 KB]
dm14_接口类-空调.py [1.0 KB]
dm11_动物多态.py [1.0 KB]
dm02_继承语法.py [565.0 B]
dm05_子类重写父类的属性和方法.py [1.1 KB]
📁 📁 06-今日总结
📁 📁 05-作业
面向对象高级作业.pdf [73.1 KB]
面向对象高级作业.md [2.9 KB]
📁 📁 02-笔记
📁 📁 02-笔记
📁 📁 images
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Python面向对象高级.md [23.2 KB]
02-笔记.zip [5.8 MB]
📁 📁 01-讲义
📁 📁 day06-多任务编程下
📁 📁 03-代码
📁 📁 样例-python其他语法
zz13_上下文管理器.py [1.1 KB]
jaychou_lyrics.txt [167.2 KB]
zz23_生成器的使用场景.py [1.1 KB]
zz31_property_装饰器方式.py [678.0 B]
zz22_yield关键字.py [487.0 B]
zz12_上下文管理器.py [989.0 B]
zz11_with基本语法.py [1.1 KB]
zz32_property_类属性方式.py [817.0 B]
zz21_生成器推导式.py [614.0 B]
📁 📁 课堂代码-其他语法
dm05_yield关键字.py [548.0 B]
dm04_生成器推导式.py [636.0 B]
dm02_上下文管理器类.py [959.0 B]
jaychou_lyrics.txt [3.3 KB]
dm06_生成器应用场景.py [1.9 KB]
dm03_上下文管理器类.py [1.6 KB]
dm01_文件读写常规.py [870.0 B]
📁 📁 课堂代码-多线程
dm11_多线程带参数边代码边音乐.py [1.2 KB]
dm15_课堂答疑设置属性.py [789.0 B]
dm14_主线程创建守候子线程.py [518.0 B]
dm10_多线程边代码边音乐.py [983.0 B]
dm17_两个线程操作同一块资源.py [744.0 B]
dm18_线程同步-加锁.py [833.0 B]
dm16_线性间共享全局变量.py [593.0 B]
dm19_死锁.py [837.0 B]
dm13_主线程会等待子线程执行完毕后在退出.py [416.0 B]
dm12_线程被调度是无序.py [667.0 B]
📁 📁 样例-多任务
zz19_为什么放在_name_.py [1.0 KB]
zz03_多进程参数边代码边音乐.py [1.1 KB]
zz13_设置守候线程.py [615.0 B]
zz06_主进程等待子进程结束后再退出.py [648.0 B]
zz10_多线程带参数边代码边音乐.py [839.0 B]
zz08_子进程主动结束子进程.py [759.0 B]
dm20_进程之间锁.py [700.0 B]
zz15_数据安全问题.py [690.0 B]
zz09_多线程边代码边音乐.py [846.0 B]
zz11_线程间是无序执行的.py [610.0 B]
zz18_other.py [1.6 KB]
zz01_单进程边代码边音乐.py [540.0 B]
zz07_子进程设置守候进程.py [766.0 B]
zz04_进程编号.py [1.6 KB]
zz12_主进程等待子线程结束后再退出.py [418.0 B]
zz17_死锁.py [895.0 B]
zz05_进程间不共享全局变量.py [1.0 KB]
zz16_互斥锁.py [786.0 B]
zz02_多进程边代码边音乐.py [1.1 KB]
zz14_线程间共享全局变量.py [745.0 B]
📁 📁 06-今日总结
📁 📁 01-讲义
06_2-线程.pdf [802.3 KB]
07_2Python其他高级语法.pptx [1.2 MB]
📁 📁 02-笔记
06_2-线程.pdf [802.3 KB]
06-多任务笔记-带图.zip [9.5 MB]
07-python高级语法笔记.zip [3.4 MB]
📁 📁 05-作业
python其他语法-作业.md [500.0 B]
多任务作业-线程相关.md [691.0 B]
📁 📁 day01-面向对象基础
📁 📁 01-讲义
01_Python面向对象基础.pdf [2.8 MB]
📁 📁 03-代码
📁 📁 样例
zz09_del魔法方法.py [1.1 KB]
zz06_无参init方法.py [649.0 B]
zz03_self关键字.py [863.0 B]
zz04_类外部添加和获取属性.py [473.0 B]
zz02_实例化对象.py [685.0 B]
zz05_类内部获取属性.py [567.0 B]
zz08_str魔法方法.py [840.0 B]
zz10_减肥.py [725.0 B]
zz01_定义类.py [384.0 B]
zz07_有参init方法.py [601.0 B]
zz11_烤地瓜.py [1.5 KB]
📁 📁 课堂
dm12_减肥小案例.py [557.0 B]
dm09_课堂答疑.py [698.0 B]
dm07_无参init方法.py [413.0 B]
dm08_有参init方法.py [441.0 B]
dm13_烤地瓜.py [1.4 KB]
dm03_self关键字-为什么要有.py [463.0 B]
dm11_del魔法方法.py [828.0 B]
dm10_str.py [606.0 B]
dm05_添加属性获取属性.py [436.0 B]
dm04_self关键字作用-类内部调用方法.py [377.0 B]
dm06_在类内部获取属性.py [481.0 B]
dm01_类的定义.py [149.0 B]
dm02_创建对象.py [525.0 B]
📁 📁 02-笔记
📁 📁 01-笔记
📁 📁 images
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开场白.md [2.0 KB]
Python面向对象基础.md [24.7 KB]
01-笔记.zip [12.7 MB]
📁 📁 05-作业
面向对象基础作业.pdf [587.2 KB]
面向对象基础作业.md [899.0 B]
day01实操练习-参考答案.md [1.6 KB]
📁 📁 06-今日总结
📁 📁 day08-数据结构和算法
📁 📁 02-笔记
08_3数据结构与算法_链表.pdf [531.9 KB]
08_4数据结构与算法_排序.pdf [2.0 MB]
📁 📁 05-作业
链表和排序-作业.md [720.0 B]
📁 📁 06-今日总结
📁 📁 03-代码
📁 📁 链表样例代码
zz03_链表.py [4.9 KB]
📁 📁 课堂代码
dm04_快速排序.py [2.2 KB]
dm02_选择排序.py [1.4 KB]
dm01_冒泡排序.py [1.4 KB]
dm03_插入排序.py [1.3 KB]
dm01_链表.py [4.7 KB]
📁 📁 排序样例代码
zz04-快速排序.py [3.0 KB]
zz042_快速排序2.py [2.6 KB]
zz52_二分查找-非递归.py [1.6 KB]
zz02_选择排序.py [1.8 KB]
zz03_插入排序.py [1.2 KB]
zz51_二分查找-递归.py [1.9 KB]
zz01_冒泡排序.py [1.7 KB]
📁 📁 01-讲义
08_4数据结构与算法_排序.pdf [2.0 MB]
08_3数据结构与算法_链表.pdf [531.9 KB]
📁 📁 day09-数据结构和算法
📁 📁 03-代码
📁 📁 样例
zz01_完全二叉树.py [4.8 KB]
📁 📁 课堂代码
dm05_二分查找递归.py [1.3 KB]
dm06_二分查找非递归.py [1.4 KB]
dm02_完全二叉树.py [5.4 KB]
📁 📁 01-讲义
08_6数据结构与算法_二叉树.pdf [2.0 MB]
08_5数据结构与算法_二分查找.pdf [558.9 KB]
📁 📁 06-今日总结
📁 📁 02-笔记
📁 📁 09-排序
📁 📁 images
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📁 📁 assets
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排序-笔记.md [7.4 KB]
📁 📁 10-二叉树
📁 📁 assets
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📁 📁 images
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二叉树-笔记.md [8.5 KB]
10-二叉树.zip [27.4 MB]
09-排序.zip [13.4 MB]
📁 📁 05-作业
二分查找法和二叉树-作业.md [599.0 B]
📁 📁 day05-网络编程下和多任务编程上
📁 📁 05-作业
网络编程作业.md [640.0 B]
多任务作业_17期.md [392.0 B]
📁 📁 02-笔记
📁 📁 06-多任务笔记
📁 📁 images
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多任务编程-课堂笔记.md [23.3 KB]
05-网络编程笔记.zip [14.4 MB]
06-多任务笔记.zip [6.8 MB]
📁 📁 03-代码
📁 📁 样例-网络编程
zz03_服务器端.py [1.3 KB]
zz02_数据类型转换.py [1.4 KB]
zz04_客户端.py [666.0 B]
zz01_socket.py [446.0 B]
zz05_服务器-支持多个客户端.py [1.3 KB]
📁 📁 课堂代码
dm08_主进程创建守候子进程.py [449.0 B]
dm02_多进程边代码边音乐.py [1.0 KB]
dm03_多进程带参数边代码边音乐.py [1.2 KB]
dm04_进程编号.py [1.6 KB]
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dm06_创建子进程的代码必须写在main进程里面.py [1.3 KB]
dm07_主进程等待子进程结束以后在结束.py [343.0 B]
dm09_主进程暴力结束子进程.py [467.0 B]
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📁 📁 样例-多进程
zz01_单进程边代码边音乐.py [540.0 B]
zz03_多进程参数边代码边音乐.py [1.1 KB]
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📁 📁 01-讲义
05_网络编程.pdf [2.9 MB]
06_1-进程.pdf [994.7 KB]
📁 📁 06-今日总结
📁 📁 day04-闭包装饰器
📁 📁 06-今日总结
📁 📁 02-笔记
📁 📁 04-笔记
📁 📁 images
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闭包和装饰器.md [16.7 KB]
📁 📁 05-笔记
📁 📁 images
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EB8EBFAC92D7A180A671B64AE24_E0B1CD5E_B517D.png [724.4 KB]
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9924BECA7429B5A4F5A68631611_B29149C4_A89FC.png [674.5 KB]
网络编程-课堂笔记.md [22.0 KB]
04-笔记.zip [4.5 MB]
05-笔记-网络编程.zip [14.4 MB]
📁 📁 01-讲义
04_闭包和装饰器.pdf [441.7 KB]
05_网络编程.pdf [2.9 MB]
📁 📁 05-作业
闭包与装饰器作业.md [807.0 B]
📁 📁 03-代码
📁 📁 样例
zz09_多个装饰器装饰同一个函数.py [600.0 B]
zz11_属性装饰器.py [1.4 KB]
zz10_装饰器带参数.py [1.3 KB]
zz03_闭包求和.py [751.0 B]
dm01_装饰器综合.py [2.0 KB]
zz01_函数名代表什么.py [658.0 B]
zz10_装饰器带参数-错误语法.py [766.0 B]
zz02_函数体内变量的保存.py [342.0 B]
zz07_装饰无参无返回值.py [2.1 KB]
zz04_内部函数修改外部函数变量.py [421.0 B]
zz08_通用不定长装饰器.py [1.0 KB]
zz06_装饰器语法糖.py [483.0 B]
zz05_装饰器基本语法.py [927.0 B]
📁 📁 课堂代码
dm11_不定长参数-装饰.py [555.0 B]
dm03_闭包的语法.py [1.0 KB]
dm05_闭包练习.py [541.0 B]
dm02_函数内如何缓存变量值.py [162.0 B]
dm综合.py [4.6 KB]
dm08_无参无返回值-装饰.py [785.0 B]
dm09_无参有返回值-装饰.py [420.0 B]
dm06_装饰器语法.py [343.0 B]
dm13_课堂答疑错误.py [571.0 B]
dm15_装饰器带参数2-正确语法.py [772.0 B]
dm04_内部函数修改外部函数的值.py [338.0 B]
dm16_装饰器带参数-纯手工的方式进行装饰.py [772.0 B]
dm14_装饰器带参数-错误语法.py [398.0 B]
dm15_装饰器带参数-正确语法.py [630.0 B]
dm07_语法糖方式使用装饰器.py [412.0 B]
dm10_有参有返回值-装饰.py [468.0 B]
dm01_直接调用间接调用.py [595.0 B]
dm12_多个装饰器修饰同一个原函数.py [628.0 B]
📁 📁 阶段015-AI智慧交通项目实战
📁 📁 04-练习代码
📁 📁 OpenCV
📁 📁 .idea
📁 📁 inspectionProfiles
Project_Default.xml [7.6 KB]
profiles_settings.xml [174.0 B]
modules.xml [264.0 B]
workspace.xml [9.5 KB]
misc.xml [195.0 B]
.gitignore [176.0 B]
OpenCV.iml [284.0 B]
📁 📁 image
littledog.jpeg [75.8 KB]
dili.jpg [45.0 KB]
dogsp.jpeg [175.6 KB]
view.jpg [29.8 KB]
rain.jpg [40.2 KB]
face.jpeg [2.5 KB]
deer.jpeg [42.4 KB]
DOG.wmv [155.5 KB]
horse.jpg [355.4 KB]
fruit.jpeg [109.0 KB]
deergray.jpeg [64.9 KB]
dogGauss.jpeg [385.2 KB]
kids.jpg [40.7 KB]
dog_10.avi [981.9 KB]
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img.jpg [177.0 KB]
dog.avi [981.9 KB]
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img.png [665.9 KB]
04-边缘检测.py [641.0 B]
out.mp4 [258.0 B]
out.avi [981.9 KB]
05-视频操作.py [472.0 B]
01-IO操作.py [511.0 B]
img_gray.png [223.7 KB]
📁 📁 yolov8
📁 📁 .idea
📁 📁 inspectionProfiles
Project_Default.xml [2.8 KB]
profiles_settings.xml [174.0 B]
.gitignore [176.0 B]
workspace.xml [8.8 KB]
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misc.xml [195.0 B]
yolov8.iml [453.0 B]
modules.xml [284.0 B]
📁 📁 __pycache__
settings.cpython-37.pyc [921.0 B]
helper.cpython-37.pyc [2.3 KB]
📁 📁 videos
video_3.mp4 [1.0 MB]
video_2.mp4 [10.6 MB]
video_1.mp4 [14.3 MB]
📁 📁 images
cat_detected.jpg [99.7 KB]
cat.jpg [115.9 KB]
kite.jpg [69.5 KB]
📁 📁 runs
📁 📁 detect
📁 📁 predict5
cat.jpg [96.4 KB]
📁 📁 predict3
cat.jpg [96.4 KB]
📁 📁 train3
📁 📁 weights
best.pt [6.2 MB]
last.pt [6.2 MB]
train_batch140.jpg [218.1 KB]
labels.jpg [97.6 KB]
confusion_matrix_normalized.png [539.8 KB]
labels_correlogram.jpg [179.1 KB]
train_batch0.jpg [394.8 KB]
train_batch141.jpg [322.9 KB]
train_batch1.jpg [347.3 KB]
results.csv [49.5 KB]
args.yaml [1.4 KB]
val_batch0_labels.jpg [375.4 KB]
train_batch2.jpg [414.0 KB]
confusion_matrix.png [533.8 KB]
results.png [273.9 KB]
val_batch0_pred.jpg [364.6 KB]
train_batch142.jpg [300.3 KB]
📁 📁 predict2
cat.jpg [96.4 KB]
📁 📁 train6
📁 📁 .ipynb_checkpoints
results-checkpoint.png [241.5 KB]
📁 📁 weights
last.pt [6.0 MB]
best.pt [6.0 MB]
results.csv [49.5 KB]
val_batch2_labels.jpg [319.6 KB]
PR_curve.png [280.1 KB]
labels_correlogram.jpg [251.2 KB]
args.yaml [1.4 KB]
train_batch0.jpg [679.0 KB]
val_batch1_labels.jpg [323.4 KB]
train_batch168140.jpg [604.5 KB]
confusion_matrix.png [187.4 KB]
train_batch2.jpg [621.1 KB]
R_curve.png [310.8 KB]
results.png [241.5 KB]
P_curve.png [225.0 KB]
val_batch0_labels.jpg [297.8 KB]
val_batch0_pred.jpg [301.6 KB]
F1_curve.png [319.4 KB]
val_batch2_pred.jpg [323.2 KB]
labels.jpg [204.4 KB]
train_batch1.jpg [653.3 KB]
confusion_matrix_normalized.png [166.0 KB]
train_batch168142.jpg [556.4 KB]
train_batch168141.jpg [502.4 KB]
val_batch1_pred.jpg [327.8 KB]
📁 📁 predict
cat.jpg [96.4 KB]
📁 📁 train5
📁 📁 weights
train_batch0.jpg [679.0 KB]
train_batch1.jpg [653.3 KB]
labels_correlogram.jpg [251.2 KB]
train_batch2.jpg [621.1 KB]
args.yaml [1.4 KB]
labels.jpg [204.4 KB]
📁 📁 train4
📁 📁 .ipynb_checkpoints
results-checkpoint.png [230.2 KB]
PR_curve-checkpoint.png [303.3 KB]
args-checkpoint.yaml [1.4 KB]
val_batch2_labels-checkpoint.jpg [319.6 KB]
val_batch1_pred-checkpoint.jpg [328.3 KB]
val_batch2_pred-checkpoint.jpg [323.1 KB]
results-checkpoint.csv [49.5 KB]
📁 📁 weights
best.pt [6.0 MB]
last.pt [6.0 MB]
F1_curve.png [322.3 KB]
train_batch2.jpg [621.1 KB]
train_batch168142.jpg [556.4 KB]
R_curve.png [319.3 KB]
train_batch1.jpg [653.3 KB]
confusion_matrix.png [198.7 KB]
P_curve.png [234.2 KB]
results.png [230.2 KB]
labels_correlogram.jpg [251.2 KB]
val_batch0_labels.jpg [297.8 KB]
val_batch0_pred.jpg [297.6 KB]
train_batch168140.jpg [604.5 KB]
PR_curve.png [303.3 KB]
val_batch2_labels.jpg [319.6 KB]
train_batch168141.jpg [502.4 KB]
val_batch2_pred.jpg [323.1 KB]
confusion_matrix_normalized.png [174.7 KB]
labels.jpg [204.4 KB]
args.yaml [1.4 KB]
val_batch1_pred.jpg [328.3 KB]
results.csv [49.5 KB]
val_batch1_labels.jpg [323.4 KB]
train_batch0.jpg [679.0 KB]
📁 📁 predict4
cat.jpg [96.4 KB]
📁 📁 train2
📁 📁 weights
args.yaml [1.4 KB]
📁 📁 train
📁 📁 weights
args.yaml [1.3 KB]
📁 📁 val
📁 📁 .ipynb_checkpoints
P_curve-checkpoint.png [234.3 KB]
val_batch2_pred-checkpoint.jpg [328.5 KB]
confusion_matrix_normalized-checkpoint.png [175.8 KB]
val_batch1_pred-checkpoint.jpg [275.1 KB]
F1_curve-checkpoint.png [322.5 KB]
val_batch2_labels-checkpoint.jpg [323.8 KB]
PR_curve-checkpoint.png [303.5 KB]
F1_curve.png [322.5 KB]
val_batch2_labels.jpg [323.8 KB]
val_batch1_pred.jpg [275.1 KB]
val_batch2_pred.jpg [328.5 KB]
PR_curve.png [303.5 KB]
confusion_matrix.png [199.1 KB]
val_batch1_labels.jpg [274.6 KB]
confusion_matrix_normalized.png [175.8 KB]
val_batch0_labels.jpg [293.5 KB]
val_batch0_pred.jpg [294.8 KB]
P_curve.png [234.3 KB]
R_curve.png [320.1 KB]
📁 📁 weights
yolov8n-cls.pt [5.3 MB]
yolov8n-seg.pt [6.7 MB]
yolov8n.pt [6.2 MB]
train.py [141.0 B]
helper.py [3.4 KB]
app.py [3.7 KB]
coco8.yaml [1.7 KB]
yolo_test.py [230.0 B]
config.yaml [263.0 B]
settings.py [1.3 KB]
val.py [206.0 B]
requirements.txt [20.0 B]
📁 📁 .idea
📁 📁 inspectionProfiles
Project_Default.xml [7.6 KB]
profiles_settings.xml [174.0 B]
workspace.xml [3.7 KB]
04-练习代码.iml [284.0 B]
modules.xml [282.0 B]
misc.xml [195.0 B]
.gitignore [176.0 B]
📁 📁 02-代码
📁 📁 ultralytics-main
📁 📁 ultralytics
📁 📁 solutions
object_counter.py [9.1 KB]
heatmap.py [10.1 KB]
__init__.py [42.0 B]
ai_gym.py [6.1 KB]
📁 📁 engine
__init__.py [42.0 B]
validator.py [14.1 KB]
model.py [19.6 KB]
exporter.py [50.0 KB]
tuner.py [11.4 KB]
predictor.py [17.4 KB]
trainer.py [33.1 KB]
results.py [22.9 KB]
📁 📁 trackers
📁 📁 utils
gmc.py [13.7 KB]
__init__.py [42.0 B]
matching.py [4.9 KB]
kalman_filter.py [14.5 KB]
basetrack.py [3.5 KB]
byte_tracker.py [18.0 KB]
bot_sort.py [8.4 KB]
track.py [2.8 KB]
__init__.py [227.0 B]
README.md [13.1 KB]
📁 📁 hub
auth.py [5.2 KB]
session.py [8.2 KB]
utils.py [9.4 KB]
__init__.py [3.6 KB]
📁 📁 utils
📁 📁 callbacks
raytune.py [608.0 B]
base.py [5.6 KB]
wb.py [6.6 KB]
mlflow.py [4.7 KB]
hub.py [3.3 KB]
neptune.py [3.6 KB]
dvc.py [4.9 KB]
clearml.py [6.1 KB]
tensorboard.py [2.8 KB]
__init__.py [214.0 B]
comet.py [13.5 KB]
tal.py [13.4 KB]
ops.py [30.6 KB]
errors.py [816.0 B]
checks.py [26.9 KB]
patches.py [2.2 KB]
plotting.py [40.7 KB]
torch_utils.py [24.0 KB]
instance.py [15.6 KB]
benchmarks.py [17.8 KB]
triton.py [3.8 KB]
__init__.py [33.0 KB]
files.py [5.2 KB]
tuner.py [6.1 KB]
autobatch.py [3.8 KB]
loss.py [25.1 KB]
downloads.py [20.5 KB]
dist.py [2.3 KB]
metrics.py [46.3 KB]
📁 📁 cfg
📁 📁 trackers
bytetrack.yaml [694.0 B]
botsort.yaml [890.0 B]
📁 📁 datasets
VOC.yaml [3.4 KB]
open-images-v7.yaml [12.1 KB]
Argoverse.yaml [2.8 KB]
coco-pose.yaml [1.5 KB]
coco128.yaml [1.8 KB]
tiger-pose.yaml [797.0 B]
GlobalWheat2020.yaml [1.9 KB]
coco8-pose.yaml [895.0 B]
DOTAv2.yaml [1.1 KB]
SKU-110K.yaml [2.4 KB]
coco128-seg.yaml [1.8 KB]
coco8.yaml [1.7 KB]
coco.yaml [2.5 KB]
xView.yaml [5.0 KB]
coco8-seg.yaml [1.8 KB]
VisDrone.yaml [2.9 KB]
ImageNet.yaml [41.4 KB]
Objects365.yaml [9.0 KB]
📁 📁 models
📁 📁 v6
yolov6.yaml [1.7 KB]
📁 📁 v5
yolov5-p6.yaml [1.9 KB]
yolov5.yaml [1.5 KB]
📁 📁 v8
yolov8-seg-p6.yaml [1.8 KB]
yolov8-cls.yaml [920.0 B]
yolov8-pose.yaml [1.5 KB]
yolov8-p2.yaml [1.7 KB]
yolov8-ghost.yaml [2.1 KB]
yolov8-ghost-p6.yaml [2.3 KB]
yolov8-seg.yaml [1.5 KB]
yolov8-rtdetr.yaml [1.9 KB]
yolov8-ghost-p2.yaml [2.3 KB]
yolov8-pose-p6.yaml [1.9 KB]
yolov8-p6.yaml [1.8 KB]
yolov8.yaml [1.9 KB]
📁 📁 v3
yolov3-tiny.yaml [1.2 KB]
yolov3.yaml [1.5 KB]
yolov3-spp.yaml [1.5 KB]
📁 📁 rt-detr
rtdetr-x.yaml [2.1 KB]
rtdetr-resnet50.yaml [1.5 KB]
rtdetr-l.yaml [1.9 KB]
rtdetr-resnet101.yaml [1.5 KB]
README.md [3.0 KB]
__init__.py [19.4 KB]
default.yaml [7.6 KB]
📁 📁 assets
bus.jpg [134.2 KB]
zidane.jpg [49.2 KB]
📁 📁 data
📁 📁 scripts
get_coco.sh [1.7 KB]
get_imagenet.sh [1.6 KB]
get_coco128.sh [619.0 B]
download_weights.sh [568.0 B]
augment.py [47.1 KB]
annotator.py [2.1 KB]
base.py [13.0 KB]
build.py [6.5 KB]
loaders.py [21.7 KB]
__init__.py [389.0 B]
converter.py [12.2 KB]
dataset.py [15.6 KB]
utils.py [29.0 KB]
📁 📁 models
📁 📁 rtdetr
__init__.py [197.0 B]
predict.py [3.3 KB]
train.py [3.7 KB]
val.py [6.5 KB]
model.py [2.1 KB]
📁 📁 yolo
📁 📁 pose
predict.py [2.5 KB]
val.py [10.4 KB]
train.py [2.8 KB]
__init__.py [199.0 B]
📁 📁 detect
train.py [5.4 KB]
__init__.py [229.0 B]
val.py [12.7 KB]
predict.py [1.6 KB]
📁 📁 segment
val.py [11.7 KB]
train.py [2.2 KB]
predict.py [2.6 KB]
__init__.py [247.0 B]
📁 📁 classify
val.py [4.8 KB]
predict.py [1.9 KB]
train.py [6.6 KB]
__init__.py [355.0 B]
model.py [1.4 KB]
__init__.py [195.0 B]
📁 📁 sam
📁 📁 modules
encoders.py [24.4 KB]
sam.py [2.7 KB]
__init__.py [42.0 B]
tiny_encoder.py [28.3 KB]
transformer.py [10.9 KB]
decoders.py [7.6 KB]
__init__.py [144.0 B]
build.py [4.8 KB]
predict.py [23.2 KB]
amg.py [7.9 KB]
model.py [4.6 KB]
📁 📁 fastsam
utils.py [2.1 KB]
predict.py [4.0 KB]
val.py [1.9 KB]
prompt.py [15.9 KB]
model.py [1.0 KB]
__init__.py [254.0 B]
📁 📁 utils
loss.py [15.6 KB]
ops.py [13.0 KB]
__init__.py [42.0 B]
📁 📁 nas
model.py [2.8 KB]
__init__.py [179.0 B]
val.py [1.8 KB]
predict.py [2.2 KB]
__init__.py [173.0 B]
📁 📁 nn
📁 📁 modules
head.py [17.9 KB]
utils.py [3.4 KB]
__init__.py [1.7 KB]
transformer.py [17.5 KB]
block.py [14.1 KB]
conv.py [12.5 KB]
autobackend.py [26.4 KB]
__init__.py [555.0 B]
tasks.py [36.6 KB]
__init__.py [463.0 B]
📁 📁 docker
Dockerfile-python [2.4 KB]
Dockerfile-arm64 [2.0 KB]
Dockerfile-cpu [2.5 KB]
Dockerfile-jetson [2.3 KB]
Dockerfile-runner [1.7 KB]
Dockerfile-conda [1.8 KB]
Dockerfile [3.6 KB]
📁 📁 examples
📁 📁 YOLOv8-OpenCV-ONNX-Python
README.md [356.0 B]
main.py [4.1 KB]
📁 📁 YOLOv8-Segmentation-ONNXRuntime-Python
main.py [13.3 KB]
README.md [2.5 KB]
📁 📁 YOLOv8-SAHI-Inference-Video
yolov8_sahi.py [4.2 KB]
readme.md [2.7 KB]
📁 📁 YOLOv8-ONNXRuntime-CPP
main.cpp [5.5 KB]
inference.h [1.8 KB]
inference.cpp [12.6 KB]
CMakeLists.txt [3.4 KB]
README.md [3.3 KB]
📁 📁 YOLOv8-ONNXRuntime
README.md [1.3 KB]
main.py [8.6 KB]
📁 📁 YOLOv8-LibTorch-CPP-Inference
main.cc [10.4 KB]
README.md [624.0 B]
CMakeLists.txt [1.7 KB]
📁 📁 YOLOv8-CPP-Inference
inference.h [2.0 KB]
README.md [1.8 KB]
main.cpp [2.2 KB]
CMakeLists.txt [547.0 B]
inference.cpp [5.5 KB]
📁 📁 YOLOv8-ONNXRuntime-Rust
📁 📁 src
main.rs [626.0 B]
yolo_result.rs [5.2 KB]
ort_backend.rs [16.4 KB]
lib.rs [3.3 KB]
model.rs [22.2 KB]
cli.rs [1.7 KB]
README.md [6.6 KB]
Cargo.toml [796.0 B]
📁 📁 YOLOv8-Region-Counter
readme.md [5.1 KB]
yolov8_region_counter.py [8.3 KB]
hub.ipynb [4.0 KB]
tutorial.ipynb [32.7 KB]
README.md [4.8 KB]
📁 📁 tests
test_cuda.py [3.4 KB]
conftest.py [3.1 KB]
test_engine.py [4.5 KB]
test_cli.py [4.9 KB]
test_python.py [17.9 KB]
test_integrations.py [4.6 KB]
📁 📁 docs
📁 📁 de
📁 📁 modes
export.md [8.6 KB]
benchmark.md [6.9 KB]
predict.md [13.8 KB]
val.md [6.3 KB]
index.md [5.2 KB]
train.md [10.6 KB]
track.md [11.5 KB]
📁 📁 datasets
index.md [9.7 KB]
📁 📁 tasks
index.md [3.6 KB]
segment.md [12.9 KB]
classify.md [11.8 KB]
detect.md [12.0 KB]
pose.md [13.1 KB]
📁 📁 models
sam.md [13.9 KB]
yolov4.md [7.2 KB]
mobile-sam.md [6.1 KB]
yolov5.md [11.6 KB]
fast-sam.md [10.3 KB]
yolov6.md [7.4 KB]
yolo-nas.md [8.3 KB]
index.md [6.1 KB]
yolov3.md [6.5 KB]
yolov8.md [19.2 KB]
rtdetr.md [6.6 KB]
yolov7.md [6.9 KB]
quickstart.md [10.8 KB]
index.md [9.7 KB]
📁 📁 ko
📁 📁 datasets
index.md [10.2 KB]
📁 📁 models
yolov5.md [11.1 KB]
yolo-nas.md [7.7 KB]
yolov8.md [18.3 KB]
fast-sam.md [10.1 KB]
rtdetr.md [6.7 KB]
index.md [6.1 KB]
yolov7.md [6.5 KB]
yolov3.md [6.0 KB]
sam.md [13.7 KB]
yolov6.md [7.2 KB]
yolov4.md [6.7 KB]
mobile-sam.md [6.2 KB]
📁 📁 modes
benchmark.md [6.8 KB]
index.md [5.2 KB]
val.md [5.8 KB]
predict.md [13.5 KB]
export.md [8.5 KB]
track.md [13.1 KB]
train.md [8.0 KB]
📁 📁 tasks
segment.md [12.6 KB]
index.md [3.4 KB]
pose.md [12.4 KB]
detect.md [12.0 KB]
classify.md [11.4 KB]
quickstart.md [12.5 KB]
index.md [9.7 KB]
📁 📁 hi
📁 📁 tasks
segment.md [16.7 KB]
index.md [6.7 KB]
pose.md [17.7 KB]
classify.md [16.5 KB]
detect.md [16.2 KB]
📁 📁 models
yolov3.md [11.4 KB]
mobile-sam.md [9.8 KB]
rtdetr.md [12.1 KB]
index.md [10.6 KB]
yolov4.md [14.0 KB]
yolo-nas.md [13.6 KB]
sam.md [24.7 KB]
yolov8.md [24.4 KB]
yolov5.md [15.8 KB]
yolov7.md [13.7 KB]
fast-sam.md [18.9 KB]
yolov6.md [12.7 KB]
📁 📁 modes
train.md [38.3 KB]
track.md [31.4 KB]
predict.md [23.2 KB]
index.md [11.0 KB]
val.md [10.8 KB]
export.md [12.8 KB]
benchmark.md [10.5 KB]
📁 📁 datasets
index.md [18.9 KB]
quickstart.md [33.8 KB]
index.md [14.7 KB]
📁 📁 en
📁 📁 usage
callbacks.md [4.7 KB]
cli.md [9.5 KB]
cfg.md [23.4 KB]
python.md [10.5 KB]
engine.md [3.2 KB]
📁 📁 modes
benchmark.md [6.7 KB]
track.md [16.2 KB]
train.md [17.8 KB]
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val.md [5.8 KB]
predict.md [40.0 KB]
📁 📁 integrations
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ray-tune.md [10.8 KB]
openvino.md [20.1 KB]
clearml.md [10.2 KB]
roboflow.md [15.9 KB]
dvc.md [9.3 KB]
mlflow.md [5.3 KB]
comet.md [8.8 KB]
📁 📁 hub
📁 📁 app
android.md [9.6 KB]
ios.md [6.8 KB]
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models.md [12.8 KB]
projects.md [11.1 KB]
integrations.md [4.6 KB]
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datasets.md [9.4 KB]
📁 📁 datasets
📁 📁 detect
argoverse.md [5.6 KB]
coco8.md [4.0 KB]
visdrone.md [5.2 KB]
index.md [5.7 KB]
open-images-v7.md [6.0 KB]
voc.md [5.3 KB]
globalwheat2020.md [5.6 KB]
coco.md [5.4 KB]
xview.md [5.1 KB]
objects365.md [4.9 KB]
sku-110k.md [4.8 KB]
📁 📁 segment
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coco.md [5.4 KB]
coco8-seg.md [4.0 KB]
📁 📁 pose
index.md [6.8 KB]
coco.md [5.2 KB]
tiger-pose.md [4.5 KB]
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📁 📁 track
index.md [913.0 B]
📁 📁 obb
dota-v2.md [5.5 KB]
index.md [3.3 KB]
📁 📁 classify
cifar10.md [3.9 KB]
imagenet10.md [4.6 KB]
fashion-mnist.md [3.7 KB]
mnist.md [4.5 KB]
caltech101.md [4.2 KB]
imagenette.md [5.3 KB]
imagewoof.md [4.8 KB]
caltech256.md [4.0 KB]
imagenet.md [4.9 KB]
cifar100.md [3.9 KB]
index.md [4.0 KB]
index.md [8.1 KB]
📁 📁 reference
📁 📁 nn
📁 📁 modules
utils.md [1.1 KB]
conv.md [1.5 KB]
block.md [1.8 KB]
head.md [1022.0 B]
transformer.md [1.5 KB]
autobackend.md [918.0 B]
tasks.md [1.5 KB]
📁 📁 engine
model.md [803.0 B]
trainer.md [787.0 B]
results.md [1.0 KB]
exporter.md [1.0 KB]
validator.md [798.0 B]
predictor.md [794.0 B]
tuner.md [938.0 B]
📁 📁 cfg
__init__.md [1.4 KB]
📁 📁 hub
utils.md [1013.0 B]
auth.md [770.0 B]
session.md [812.0 B]
__init__.md [1.0 KB]
📁 📁 solutions
ai_gym.md [955.0 B]
object_counter.md [1.0 KB]
heatmap.md [957.0 B]
📁 📁 trackers
📁 📁 utils
gmc.md [833.0 B]
matching.md [1.1 KB]
kalman_filter.md [943.0 B]
bot_sort.md [914.0 B]
byte_tracker.md [929.0 B]
basetrack.md [891.0 B]
track.md [956.0 B]
📁 📁 utils
📁 📁 callbacks
hub.md [1.3 KB]
clearml.md [1.3 KB]
mlflow.md [1.0 KB]
tensorboard.md [1.3 KB]
wb.md [1.2 KB]
dvc.md [1.4 KB]
comet.md [2.6 KB]
base.md [2.7 KB]
neptune.md [1.4 KB]
raytune.md [888.0 B]
errors.md [818.0 B]
instance.md [884.0 B]
loss.md [1.2 KB]
patches.md [931.0 B]
dist.md [1019.0 B]
autobatch.md [910.0 B]
benchmarks.md [881.0 B]
ops.md [2.4 KB]
tuner.md [788.0 B]
downloads.md [1.4 KB]
metrics.md [1.8 KB]
triton.md [811.0 B]
files.md [1.2 KB]
checks.md [2.2 KB]
plotting.md [1.3 KB]
torch_utils.md [2.5 KB]
__init__.md [2.5 KB]
tal.md [1.1 KB]
📁 📁 data
build.md [1.2 KB]
augment.md [1.8 KB]
base.md [752.0 B]
utils.md [1.6 KB]
annotator.md [822.0 B]
loaders.md [1.2 KB]
converter.md [1.1 KB]
dataset.md [1.0 KB]
📁 📁 models
📁 📁 sam
📁 📁 modules
…（已达最大深度 10 层，子目录未展开）
amg.md [1.5 KB]
build.md [1.1 KB]
predict.md [848.0 B]
model.md [814.0 B]
📁 📁 yolo
📁 📁 segment
…（已达最大深度 10 层，子目录未展开）
📁 📁 pose
…（已达最大深度 10 层，子目录未展开）
📁 📁 detect
…（已达最大深度 10 层，子目录未展开）
📁 📁 classify
…（已达最大深度 10 层，子目录未展开）
model.md [802.0 B]
📁 📁 rtdetr
model.md [830.0 B]
predict.md [843.0 B]
val.md [906.0 B]
train.md [893.0 B]
📁 📁 fastsam
predict.md [909.0 B]
prompt.md [847.0 B]
val.md [818.0 B]
model.md [824.0 B]
utils.md [941.0 B]
📁 📁 nas
model.md [805.0 B]
predict.md [839.0 B]
val.md [821.0 B]
📁 📁 utils
ops.md [896.0 B]
loss.md [921.0 B]
📁 📁 models
yolov4.md [6.3 KB]
yolov6.md [6.6 KB]
rtdetr.md [6.0 KB]
fast-sam.md [9.4 KB]
yolov8.md [18.0 KB]
yolov7.md [5.9 KB]
yolov3.md [5.8 KB]
sam.md [12.7 KB]
yolo-nas.md [7.5 KB]
mobile-sam.md [5.7 KB]
index.md [5.3 KB]
yolov5.md [10.7 KB]
📁 📁 yolov5
📁 📁 tutorials
multi_gpu_training.md [11.2 KB]
clearml_logging_integration.md [10.9 KB]
neural_magic_pruning_quantization.md [10.8 KB]
roboflow_datasets_integration.md [5.2 KB]
model_export.md [14.9 KB]
model_ensembling.md [10.1 KB]
test_time_augmentation.md [10.7 KB]
comet_logging_integration.md [10.8 KB]
train_custom_data.md [16.8 KB]
hyperparameter_evolution.md [10.9 KB]
running_on_jetson_nano.md [10.0 KB]
architecture_description.md [12.0 KB]
transfer_learning_with_frozen_layers.md [7.5 KB]
model_pruning_and_sparsity.md [8.6 KB]
tips_for_best_training_results.md [6.9 KB]
pytorch_hub_model_loading.md [14.4 KB]
📁 📁 environments
aws_quickstart_tutorial.md [6.4 KB]
azureml_quickstart_tutorial.md [2.8 KB]
google_cloud_quickstart_tutorial.md [6.0 KB]
docker_image_quickstart_tutorial.md [3.4 KB]
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📁 📁 guides
triton-inference-server.md [5.0 KB]
yolo-common-issues.md [16.8 KB]
region-counting.md [5.0 KB]
azureml-quickstart.md [7.4 KB]
raspberry-pi.md [8.2 KB]
security-alarm-system.md [6.0 KB]
workouts-monitoring.md [8.0 KB]
vision-eye.md [5.5 KB]
conda-quickstart.md [5.2 KB]
docker-quickstart.md [4.5 KB]
isolating-segmentation-objects.md [14.7 KB]
index.md [6.2 KB]
yolo-performance-metrics.md [10.9 KB]
instance-segmentation-and-tracking.md [5.8 KB]
model-deployment-options.md [22.8 KB]
kfold-cross-validation.md [12.3 KB]
yolo-thread-safe-inference.md [5.2 KB]
object-counting.md [9.9 KB]
sahi-tiled-inference.md [7.4 KB]
heatmaps.md [14.7 KB]
hyperparameter-tuning.md [9.6 KB]
📁 📁 help
code_of_conduct.md [5.5 KB]
FAQ.md [3.0 KB]
security.md [3.2 KB]
CI.md [11.4 KB]
CLA.md [5.8 KB]
index.md [2.4 KB]
environmental-health-safety.md [3.2 KB]
minimum_reproducible_example.md [3.6 KB]
privacy.md [7.9 KB]
contributing.md [5.3 KB]
📁 📁 tasks
index.md [3.0 KB]
segment.md [11.7 KB]
detect.md [11.0 KB]
pose.md [11.9 KB]
classify.md [11.1 KB]
quickstart.md [18.5 KB]
index.md [8.8 KB]
robots.txt [583.0 B]
CNAME [21.0 B]
📁 📁 zh
📁 📁 modes
val.md [4.9 KB]
track.md [12.5 KB]
benchmark.md [6.1 KB]
export.md [7.4 KB]
train.md [16.0 KB]
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📁 📁 models
rtdetr.md [5.4 KB]
yolov5.md [10.3 KB]
yolov3.md [5.2 KB]
yolov8.md [17.0 KB]
mobile-sam.md [5.3 KB]
fast-sam.md [8.5 KB]
yolo-nas.md [6.7 KB]
yolov4.md [5.3 KB]
sam.md [11.6 KB]
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📁 📁 datasets
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quickstart.md [16.6 KB]
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📁 📁 ja
📁 📁 modes
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📁 📁 models
yolov4.md [7.8 KB]
sam.md [15.6 KB]
yolov3.md [7.0 KB]
yolov8.md [19.4 KB]
mobile-sam.md [6.7 KB]
rtdetr.md [7.5 KB]
yolo-nas.md [8.5 KB]
fast-sam.md [11.5 KB]
yolov6.md [7.9 KB]
index.md [6.8 KB]
yolov7.md [7.1 KB]
yolov5.md [12.2 KB]
📁 📁 tasks
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📁 📁 datasets
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📁 📁 ar
📁 📁 tasks
pose.md [13.9 KB]
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📁 📁 datasets
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📁 📁 models
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yolov8.md [20.9 KB]
yolo-nas.md [9.9 KB]
mobile-sam.md [7.4 KB]
yolov3.md [7.6 KB]
yolov4.md [8.6 KB]
sam.md [17.0 KB]
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📁 📁 fr
📁 📁 modes
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📁 📁 models
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sam.md [15.0 KB]
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yolov3.md [6.8 KB]
fast-sam.md [10.8 KB]
yolo-nas.md [8.7 KB]
yolov6.md [7.7 KB]
rtdetr.md [7.1 KB]
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📁 📁 datasets
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📁 📁 ru
📁 📁 datasets
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📁 📁 models
yolo-nas.md [12.4 KB]
index.md [8.9 KB]
fast-sam.md [15.3 KB]
yolov5.md [14.6 KB]
sam.md [21.7 KB]
rtdetr.md [9.7 KB]
mobile-sam.md [9.1 KB]
yolov3.md [9.5 KB]
yolov4.md [10.8 KB]
yolov7.md [10.8 KB]
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📁 📁 overrides
📁 📁 partials
source-file.html [858.0 B]
comments.html [1.7 KB]
📁 📁 assets
favicon.ico [9.4 KB]
📁 📁 stylesheets
style.css [1.2 KB]
📁 📁 javascript
extra.js [3.1 KB]
📁 📁 pt
📁 📁 modes
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val.md [6.2 KB]
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📁 📁 models
sam.md [14.5 KB]
fast-sam.md [10.3 KB]
yolov8.md [19.2 KB]
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yolov3.md [6.4 KB]
yolov5.md [11.5 KB]
rtdetr.md [6.6 KB]
yolov4.md [7.0 KB]
mobile-sam.md [6.1 KB]
yolov6.md [7.3 KB]
yolov7.md [6.8 KB]
yolo-nas.md [8.2 KB]
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📁 📁 modes
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📁 📁 models
yolov7.md [7.0 KB]
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yolov8.md [18.9 KB]
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📁 📁 .idea
📁 📁 inspectionProfiles
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profiles_settings.xml [174.0 B]
workspace.xml [1.9 KB]
misc.xml [195.0 B]
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modules.xml [284.0 B]
ultralytics-main.iml [555.0 B]
📁 📁 .github
📁 📁 workflows
links.yml [2.9 KB]
greetings.yml [4.9 KB]
docker.yaml [6.2 KB]
publish.yml [5.7 KB]
cla.yml [1.4 KB]
stale.yml [2.3 KB]
codeql.yaml [1.2 KB]
ci.yaml [10.8 KB]
📁 📁 ISSUE_TEMPLATE
bug-report.yml [3.3 KB]
question.yml [1.2 KB]
config.yml [363.0 B]
feature-request.yml [1.8 KB]
dependabot.yml [647.0 B]
setup.py [4.2 KB]
CITATION.cff [612.0 B]
LICENSE [33.7 KB]
.pre-commit-config.yaml [2.3 KB]
setup.cfg [2.0 KB]
.gitignore [2.2 KB]
MANIFEST.in [200.0 B]
requirements.txt [1.4 KB]
CONTRIBUTING.md [5.5 KB]
README.zh-CN.md [27.8 KB]
README.md [28.7 KB]
📁 📁 yolov8-tracking-count
📁 📁 Videos
traffic1.mp4 [4.7 MB]
traffic2.mp4 [33.7 MB]
📁 📁 __pycache__
utils.cpython-37.pyc [1.3 KB]
sort.cpython-37.pyc [9.3 KB]
📁 📁 static
main_counter.png [15.4 KB]
in.png [19.6 KB]
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📁 📁 .idea
📁 📁 inspectionProfiles
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profiles_settings.xml [174.0 B]
workspace.xml [10.2 KB]
modules.xml [294.0 B]
.gitignore [176.0 B]
yolov8-tracking-count.iml [441.0 B]
misc.xml [195.0 B]
📁 📁 weights
yolov8l.pt [83.7 MB]
yolov8n.pt [6.2 MB]
yolov8s.pt [21.5 MB]
📁 📁 output
sort.py [10.3 KB]
trackingwithSort.py [5.2 KB]
utils.py [1.4 KB]
requirements.txt [60.0 B]
trackingwithdeepsort.py [6.4 KB]
getXY.py [960.0 B]
kalmanfilter.py [3.7 KB]
car_img.jpg [198.9 KB]
📁 📁 yolov8
📁 📁 runs
📁 📁 detect
📁 📁 train3
📁 📁 weights
best.pt [6.2 MB]
last.pt [6.2 MB]
train_batch140.jpg [218.1 KB]
args.yaml [1.4 KB]
confusion_matrix.png [533.8 KB]
labels.jpg [97.6 KB]
train_batch0.jpg [394.8 KB]
val_batch0_pred.jpg [364.6 KB]
train_batch1.jpg [347.3 KB]
train_batch2.jpg [414.0 KB]
results.png [273.9 KB]
val_batch0_labels.jpg [375.4 KB]
train_batch141.jpg [322.9 KB]
train_batch142.jpg [300.3 KB]
confusion_matrix_normalized.png [539.8 KB]
results.csv [49.5 KB]
labels_correlogram.jpg [179.1 KB]
📁 📁 train2
📁 📁 weights
args.yaml [1.4 KB]
📁 📁 val
📁 📁 .ipynb_checkpoints
val_batch2_labels-checkpoint.jpg [323.8 KB]
confusion_matrix_normalized-checkpoint.png [175.8 KB]
P_curve-checkpoint.png [234.3 KB]
val_batch2_pred-checkpoint.jpg [328.5 KB]
PR_curve-checkpoint.png [303.5 KB]
F1_curve-checkpoint.png [322.5 KB]
val_batch1_pred-checkpoint.jpg [275.1 KB]
R_curve.png [320.1 KB]
val_batch2_pred.jpg [328.5 KB]
val_batch0_labels.jpg [293.5 KB]
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val_batch0_pred.jpg [294.8 KB]
val_batch1_labels.jpg [274.6 KB]
F1_curve.png [322.5 KB]
PR_curve.png [303.5 KB]
confusion_matrix_normalized.png [175.8 KB]
val_batch2_labels.jpg [323.8 KB]
P_curve.png [234.3 KB]
confusion_matrix.png [199.1 KB]
📁 📁 predict2
cat.jpg [96.4 KB]
📁 📁 train4
📁 📁 .ipynb_checkpoints
val_batch2_pred-checkpoint.jpg [323.1 KB]
results-checkpoint.csv [49.5 KB]
val_batch2_labels-checkpoint.jpg [319.6 KB]
results-checkpoint.png [230.2 KB]
val_batch1_pred-checkpoint.jpg [328.3 KB]
args-checkpoint.yaml [1.4 KB]
PR_curve-checkpoint.png [303.3 KB]
📁 📁 weights
last.pt [6.0 MB]
best.pt [6.0 MB]
val_batch0_labels.jpg [297.8 KB]
labels.jpg [204.4 KB]
PR_curve.png [303.3 KB]
train_batch168141.jpg [502.4 KB]
train_batch0.jpg [679.0 KB]
confusion_matrix.png [198.7 KB]
P_curve.png [234.2 KB]
train_batch168142.jpg [556.4 KB]
results.png [230.2 KB]
train_batch2.jpg [621.1 KB]
labels_correlogram.jpg [251.2 KB]
val_batch0_pred.jpg [297.6 KB]
val_batch1_labels.jpg [323.4 KB]
train_batch1.jpg [653.3 KB]
val_batch2_labels.jpg [319.6 KB]
val_batch2_pred.jpg [323.1 KB]
F1_curve.png [322.3 KB]
results.csv [49.5 KB]
confusion_matrix_normalized.png [174.7 KB]
train_batch168140.jpg [604.5 KB]
val_batch1_pred.jpg [328.3 KB]
R_curve.png [319.3 KB]
args.yaml [1.4 KB]
📁 📁 predict5
cat.jpg [96.4 KB]
📁 📁 predict3
cat.jpg [96.4 KB]
📁 📁 predict
cat.jpg [96.4 KB]
📁 📁 train
📁 📁 weights
args.yaml [1.3 KB]
📁 📁 predict4
cat.jpg [96.4 KB]
📁 📁 train6
📁 📁 weights
last.pt [6.0 MB]
best.pt [6.0 MB]
📁 📁 .ipynb_checkpoints
results-checkpoint.png [241.5 KB]
val_batch0_pred.jpg [301.6 KB]
confusion_matrix_normalized.png [166.0 KB]
labels.jpg [204.4 KB]
val_batch1_labels.jpg [323.4 KB]
train_batch168140.jpg [604.5 KB]
confusion_matrix.png [187.4 KB]
PR_curve.png [280.1 KB]
train_batch2.jpg [621.1 KB]
R_curve.png [310.8 KB]
val_batch0_labels.jpg [297.8 KB]
train_batch168142.jpg [556.4 KB]
val_batch1_pred.jpg [327.8 KB]
results.csv [49.5 KB]
args.yaml [1.4 KB]
labels_correlogram.jpg [251.2 KB]
train_batch168141.jpg [502.4 KB]
results.png [241.5 KB]
P_curve.png [225.0 KB]
val_batch2_labels.jpg [319.6 KB]
train_batch0.jpg [679.0 KB]
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📁 📁 train5
📁 📁 weights
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train_batch2.jpg [621.1 KB]
args.yaml [1.4 KB]
labels_correlogram.jpg [251.2 KB]
train_batch1.jpg [653.3 KB]
📁 📁 images
kite.jpg [69.5 KB]
cat_detected.jpg [99.7 KB]
cat.jpg [115.9 KB]
📁 📁 .idea
📁 📁 inspectionProfiles
Project_Default.xml [2.8 KB]
profiles_settings.xml [174.0 B]
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📁 📁 videos
video_3.mp4 [1.0 MB]
video_1.mp4 [14.3 MB]
video_2.mp4 [10.6 MB]
📁 📁 __pycache__
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helper.cpython-37.pyc [2.3 KB]
📁 📁 weights
yolov8n-seg.pt [6.7 MB]
yolov8n.pt [6.2 MB]
yolov8n-cls.pt [5.3 MB]
train.py [221.0 B]
yolo_test.py [230.0 B]
val.py [206.0 B]
coco8.yaml [1.7 KB]
config.yaml [263.0 B]
yolov8n.yaml [1.9 KB]
helper.py [3.4 KB]
settings.py [1.3 KB]
app.py [3.7 KB]
requirements.txt [20.0 B]
📁 📁 laneDection
📁 📁 .idea
📁 📁 inspectionProfiles
Project_Default.xml [7.6 KB]
.gitignore [176.0 B]
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laneDection.iml [445.0 B]
vcs.xml [192.0 B]
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📁 📁 camera_cal
calibration20.jpg [115.6 KB]
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calibration12.jpg [114.1 KB]
calibration1.jpg [123.5 KB]
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calibration15.jpg [96.7 KB]
calibration3.jpg [140.8 KB]
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calibration14.jpg [95.5 KB]
calibration5.jpg [134.3 KB]
calibration13.jpg [111.8 KB]
calibration16.jpg [97.3 KB]
calibration4.jpg [140.0 KB]
calibration18.jpg [87.0 KB]
calibration10.jpg [139.3 KB]
calibration11.jpg [122.9 KB]
calibration6.jpg [129.4 KB]
calibration9.jpg [123.4 KB]
calibration7.jpg [110.3 KB]
calibration17.jpg [95.9 KB]
📁 📁 test
test1.jpg [212.1 KB]
test3.jpg [144.1 KB]
test2.jpg [170.1 KB]
straight_lines2.jpg [188.6 KB]
test6.jpg [226.7 KB]
straight_lines2_line.jpg [231.2 KB]
test5.jpg [238.2 KB]
frame45.jpg [196.5 KB]
straight_lines1.jpg [151.4 KB]
straight_lines2_out.jpg [232.5 KB]
main.py [13.6 KB]
output123.mp4 [139.0 KB]
project_video.mp4 [24.1 MB]
output456.mp4 [1.8 MB]
output.mp4 [2.8 MB]
📁 📁 OpenCV
📁 📁 image
📁 📁 .ipynb_checkpoints
04.图像的算数运算-checkpoint.ipynb [72.0 B]
horse.jpg [355.4 KB]
deer.jpeg [42.4 KB]
fruit.jpeg [109.0 KB]
rain.jpg [40.2 KB]
face.jpeg [2.5 KB]
deergray.jpeg [64.9 KB]
dogsp.jpeg [175.6 KB]
view.jpg [29.8 KB]
kids.jpg [40.7 KB]
dogGauss.jpeg [385.2 KB]
DOG.wmv [155.5 KB]
littledog.jpeg [75.8 KB]
dili.jpg [45.0 KB]
📁 📁 .idea
📁 📁 inspectionProfiles
Project_Default.xml [2.8 KB]
profiles_settings.xml [174.0 B]
workspace.xml [9.9 KB]
misc.xml [195.0 B]
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OpenCV.iml [284.0 B]
modules.xml [264.0 B]
02-图像加法.py [460.0 B]
dog.avi [981.9 KB]
01-IO操作.py [691.0 B]
05-边缘检测.py [644.0 B]
04-图像平滑.py [744.0 B]
03.几何变换.py [1.4 KB]
06-视频读写.py [520.0 B]
dog_10.avi [981.9 KB]
📁 📁 image
📁 📁 .ipynb_checkpoints
04.图像的算数运算-checkpoint.ipynb [72.0 B]
dili.jpg [45.0 KB]
deer.jpeg [42.4 KB]
littledog.jpeg [75.8 KB]
face.jpeg [2.5 KB]
fruit.jpeg [109.0 KB]
kids.jpg [40.7 KB]
DOG.wmv [155.5 KB]
view.jpg [29.8 KB]
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dogsp.jpeg [175.6 KB]
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horse.jpg [355.4 KB]
📁 📁 01-讲义
📁 📁 site
📁 📁 简介
📁 📁 README.assets
image-20200430092045062.png [169.2 KB]
index.html [19.3 KB]
📁 📁 项目简介
📁 📁 ReadMe
index.html [21.9 KB]
📁 📁 ReadMe.assets
output.mp4 [23.8 MB]
lineoutput.mp4 [17.7 MB]
image-20200429223754471.png [317.0 KB]
carout.mp4 [218.6 KB]
📁 📁 yoloV8
📁 📁 images
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📁 📁 04-训练自己的V8模型
index.html [71.9 KB]
📁 📁 01-V8简介
index.html [46.5 KB]
📁 📁 03-V8详解
index.html [31.9 KB]
📁 📁 02-效果展示
index.html [62.6 KB]
📁 📁 OpenCV
📁 📁 05-边缘检测
index.html [48.7 KB]
📁 📁 04-平滑方法
index.html [40.6 KB]
📁 📁 01-OpenCV简介
index.html [23.1 KB]
📁 📁 06-视频读写
index.html [29.4 KB]
📁 📁 02-基本操作
index.html [46.2 KB]
📁 📁 03-几何变换
index.html [52.6 KB]
📁 📁 assets
image-20190928102302238.png [555.5 KB]
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📁 📁 assets
📁 📁 images
favicon.png [1.8 KB]
📁 📁 javascripts
📁 📁 lunr
📁 📁 min
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wordcut.js [661.6 KB]
📁 📁 workers
search.74e28a9f.min.js [38.0 KB]
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bundle.220ee61c.min.js [110.9 KB]
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📁 📁 stylesheets
main.eebd395e.min.css [110.8 KB]
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📁 📁 跟踪算法
📁 📁 09-sort完成目标跟踪
index.html [58.7 KB]
📁 📁 04-匈牙利匹配
index.html [32.0 KB]
📁 📁 03-sort与deepsort
index.html [22.9 KB]
📁 📁 07-sort算法实现
index.html [110.4 KB]
📁 📁 06-卡尔曼滤波实践
index.html [68.5 KB]
📁 📁 01-车流量统计流程思想
index.html [21.8 KB]
📁 📁 02-多目标跟踪
index.html [30.6 KB]
📁 📁 10-deepsort
index.html [64.2 KB]
📁 📁 08-utils
index.html [26.3 KB]
📁 📁 images
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📁 📁 05-卡尔曼滤波
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📁 📁 calibrateT
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📁 📁 ReadMe
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📁 📁 main
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📁 📁 05-笔记
📁 📁 images
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📁 📁 阶段7-自然语言处理基础
📁 📁 day04_案例人名分类器
📁 📁 6.今日总结
03-人名分类器 实现分析.xmind [882.0 KB]
📁 📁 2.笔记
📁 📁 人名分类器课堂纪要
📁 📁 img
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📁 📁 tmp
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人名分类器纪要.md [1.7 KB]
📁 📁 RNN+注意力机制课堂纪要
📁 📁 img2
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rnn课堂纪要.md [3.3 KB]
📁 📁 1.讲义
02-bmm意义解读.png [315.8 KB]
服务器上模型训练操作实战2.pdf [2.4 MB]
实验课GPU设备上模型训练.pdf [2.2 MB]
01-注意力机制图-数据形状.png [123.7 KB]
05-RNN案例人名分类器.pdf [3.0 MB]
📁 📁 5.作业
day04作业.md [463.0 B]
📁 📁 3.代码
📁 📁 预习代码
📁 📁 img
rnn_lstm_gru_loss2.png [28.2 KB]
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📁 📁 data
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📁 📁 model
samp15_name_classfication_gpu.py [32.6 KB]
📁 📁 课堂代码
dm04_attentionkey10.py [4.2 KB]
📁 📁 day06_注意力机制seq2seq
📁 📁 6.今日总结
📁 📁 1.讲义
07-案例Seq2Seq英译法案例.pdf [2.6 MB]
📁 📁 3.代码
📁 📁 samp02_seq2seq_evaluate
📁 📁 data
eng-fra-v2.txt [544.9 KB]
📁 📁 gpumodel
my_attndecoderrnn.pth [10.5 MB]
s2s_loss.png [8.0 KB]
my_encoderrnn.pth [4.2 MB]
samp02_seq2seq_evaluate.py [19.1 KB]
samp03_seq2seq_train-gpu.py [26.9 KB]
samp01_seq2seq_lx.py [26.6 KB]
📁 📁 5.作业
day06作业.md [521.0 B]
📁 📁 2.笔记
📁 📁 tranformer笔记课堂纪要
📁 📁 img
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📁 📁 img2
image-20231029120606613.png [1.3 MB]
image-20231029115557494.png [749.3 KB]
image-20231029112227496.png [977.9 KB]
课堂纪要.md [12.5 KB]
transformer-Attention is All You Need.pdf [2.1 MB]
📁 📁 day01_NLP概述-文本预处理上
📁 📁 3.代码
📁 📁 预习代码
📁 📁 cn_data
dev.tsv [236.9 KB]
SimHei.ttf [9.6 MB]
train.tsv [707.4 KB]
📁 📁 data
wikifil.pl [1.9 KB]
vocab100.csv [1.0 KB]
enwik9.zip [307.6 MB]
userdict.txt [76.0 B]
samp01_jieba分词.py [2.8 KB]
samp02_文本张量onehot.py [3.0 KB]
samp03_fasttext训练词向量.py [4.0 KB]
samp04_embedding词嵌入层.py [5.9 KB]
📁 📁 课堂代码
dm03_fasttext训练词向量.py [2.5 KB]
dm04_词向量可视化.py [3.0 KB]
dm02_文本张量onehot.py [3.1 KB]
dm01_jieba分词.py [2.5 KB]
📁 📁 1.讲义
02-文本预处理-上.pdf [2.1 MB]
01-自然语言处理概念.pdf [1.2 MB]
📁 📁 7.pycharm工具包
📁 📁 pycharm
pycharm-professional-2021.2.1.dmg [598.3 MB]
pycharm-professional-2021.2.1.exe [463.6 MB]
📁 📁 2.笔记
01-配置PyCharm连接远程服务器解释器.pdf [6.8 MB]
📁 📁 6.今日总结
📁 📁 5.作业
day01作业.md [590.0 B]
📁 📁 day10_迁移学习案例实战
📁 📁 2.笔记
📁 📁 迁移学习授课笔记
📁 📁 img2
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📁 📁 img
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image-20220613151413943.png [1.1 MB]
image-20220611160133975.png [286.6 KB]
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glue表格.xlsx [11.2 KB]
glue任务识别表.png [813.1 KB]
迁移学习案例纪要.md [2.9 KB]
📁 📁 5.作业
day10作业.md [321.0 B]
📁 📁 1.讲义
11-迁移学习-下.pptx [1.8 MB]
📁 📁 6.今日总结
📁 📁 3.代码
📁 📁 预习代码
📁 📁 mydata1
train.csv [2.9 MB]
test.csv [361.5 KB]
validation.csv [365.9 KB]
samp21_classfication_v2.py [14.2 KB]
samp41_nsp_v2.py [9.9 KB]
samp31_mask_v2.py [12.3 KB]
📁 📁 课堂代码
dm21_bert_class.py [8.6 KB]
dm23_nsp.py [4.6 KB]
dm22_mask.py [7.9 KB]
📁 📁 day02_文本预处理下
📁 📁 5.作业
day02作业.md [948.0 B]
📁 📁 1.讲义
03-文本预处理-下.pdf [1.5 MB]
📁 📁 2.笔记
📁 📁 文本预处理纪要
📁 📁 img2
image-20231022085418087.png [1.6 MB]
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课堂纪要day01.md [8.5 KB]
📁 📁 6.今日总结
01-文本预处理知识体系梳理.xmind [2.8 MB]
📁 📁 3.代码
📁 📁 课堂代码
dm05_文本数据分析.py [4.6 KB]
dm06_添加常见特征处理.py [411.0 B]
dm01_rnn.py [9.7 KB]
📁 📁 预习代码
samp05_文本数据分析.py [10.0 KB]
samp06_文本特征处理.py [2.2 KB]
samp07_文本数据增强.py [1.4 KB]
📁 📁 day09_迁移学习transformers
📁 📁 5.作业
day09作业.md [337.0 B]
📁 📁 6.今日总结
📁 📁 2.笔记
📁 📁 fasttext课堂纪要
📁 📁 img2
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📁 📁 img
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image-20220611164030843.png [560.2 KB]
image-20211030145750540.png [157.0 KB]
image-20220611150821202.png [571.2 KB]
fasttext纪要.md [16.6 KB]
glue任务识别表.png [813.1 KB]
glue表格.xlsx [11.2 KB]
📁 📁 3.代码
📁 📁 课堂代码
dm03_specmodel.py [1.6 KB]
dm02_automodel.py [7.7 KB]
dm01_pipline.py [4.7 KB]
📁 📁 预习代码
📁 📁 mydata1
train.csv [2.9 MB]
test.csv [361.5 KB]
validation.csv [365.9 KB]
📁 📁 bert-base-chinese
pytorch_model.bin [392.5 MB]
.gitattributes [391.0 B]
flax_model.msgpack [390.2 MB]
tokenizer.json [262.6 KB]
vocab.txt [107.0 KB]
tokenizer_config.json [29.0 B]
config.json [624.0 B]
tf_model.h5 [456.2 MB]
README.md [21.0 B]
samp12_automodel.py [17.1 KB]
samp11_pipline.py [9.1 KB]
samp13_spec_model2.py [6.0 KB]
📁 📁 1.讲义
10-迁移学习-中.pdf [3.4 MB]
📁 📁 day03_RNN及其变体
📁 📁 5.作业
day03作业.md [524.0 B]
📁 📁 6.今日总结
02-RNN及其变体知识体系梳理.xmind [4.7 MB]
📁 📁 3.代码
📁 📁 课堂代码
dm01_nameclass-数据处理三部曲.py [4.1 KB]
dm01_nameclass-添加rnn.py [6.6 KB]
dm01_nameclass.py [21.0 KB]
📁 📁 预习代码
samp01_nameclass.py [26.4 KB]
samp03_gru.py [1.2 KB]
samp01_rnn.py [8.2 KB]
samp02_lstm.py [1.3 KB]
📁 📁 2.笔记
📁 📁 人名分类器课堂纪要
📁 📁 img2
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📁 📁 img
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📁 📁 tmp
1个1个的送入和10个单词一次性输入结果相等实验.png [704.8 KB]
20211017.png [247.0 KB]
注意力计算图.png [80.4 KB]
人名分类器纪要.md [1.6 KB]
📁 📁 RNN课堂纪要
📁 📁 img
image-20211017163946569.png [913.7 KB]
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📁 📁 img2
image-20231023163354225.png [913.5 KB]
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rnn课堂纪要.md [2.6 KB]
📁 📁 1.讲义
04-RNN及其变体.pdf [3.0 MB]
📁 📁 day05_注意力机制seq2seq
📁 📁 3.代码
📁 📁 预习代码
📁 📁 gpumodel
my_encoderrnn.pth [4.2 MB]
s2s_loss.png [8.0 KB]
my_attndecoderrnn.pth [10.5 MB]
📁 📁 data
eng-fra-v2.txt [544.9 KB]
samp01_seq2seq_lx.py [26.6 KB]
📁 📁 课堂代码
dm01_seq2seq.py [24.0 KB]
📁 📁 1.讲义
07-案例Seq2Seq英译法案例.pdf [2.6 MB]
06-注意力机制介绍.pdf [1.7 MB]
📁 📁 6.今日总结
📁 📁 2.笔记
📁 📁 seq2seq授课课堂纪要
📁 📁 img2
image-20231028172620220.png [959.0 KB]
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📁 📁 img
image-20211020112027377.png [923.3 KB]
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课堂纪要seq2seq.md [3.6 KB]
📁 📁 5.作业
day05作业.md [445.0 B]
📁 📁 day07_Transformer
📁 📁 1.讲义
transformer-Attention is All You Need.pdf [2.1 MB]
08-Tranformer架构和实现.pdf [7.3 MB]
📁 📁 6.今日总结
📁 📁 3.代码
📁 📁 预习代码
samp01_input.py [7.9 KB]
samp02_encode.py [20.5 KB]
samp02_other.py [980.0 B]
samp04_makemodel.py [28.0 KB]
samp03_decoder.py [14.1 KB]
📁 📁 课堂代码
dm01_input.py [3.6 KB]
dm03_decode.py [15.5 KB]
dm04_makemodel.py [25.6 KB]
dm02_encode.py [17.4 KB]
📁 📁 5.作业
day07作业.md [367.0 B]
📁 📁 2.笔记
📁 📁 tranformer笔记课堂纪要
📁 📁 img2
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📁 📁 img
nlp_nlg_nlu.png [169.8 KB]
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课堂纪要.md [13.0 KB]
transformer-Attention is All You Need.pdf [2.1 MB]
📁 📁 day08_fasttext分类-词向量迁移
📁 📁 6.今日总结
📁 📁 1.讲义
09-迁移学习-上.pdf [2.9 MB]
📁 📁 5.作业
day08作业.md [340.0 B]
📁 📁 3.代码
📁 📁 课堂代码
dm01_fasttextapi.py [1.4 KB]
📁 📁 预习代码
📁 📁 fasttext_data
📁 📁 .idea
📁 📁 inspectionProfiles
profiles_settings.xml [174.0 B]
modules.xml [285.0 B]
workspace.xml [308.0 B]
encodings.xml [135.0 B]
fasttext_data.iml [291.0 B]
cooking.stackexchange.txt [1.3 MB]
cn_test_fast.txt [865.6 KB]
model_cooking.bin [6.1 MB]
cn_dev_fast1.txt [777.4 KB]
cooking.stackexchange.id [88.0 KB]
cooking.pre.train [1.1 MB]
readme.txt [743.0 B]
cn_test_fast1.txt [778.1 KB]
cooking.preprocessed.txt [1.4 MB]
cn_train_fast.txt [15.2 MB]
cooking.valid [266.0 KB]
cn_train_fast1.txt [13.7 MB]
cooking.pre.valid [271.7 KB]
cooking.train [1.1 MB]
📁 📁 data
cc.zh.300.bin [1.9 GB]
samp02_wordvector_transfer.py [2.1 KB]
samp01_classfication.py [6.3 KB]
📁 📁 2.笔记
📁 📁 fasttext课堂纪要
📁 📁 img
image-20220611120104903.png [75.4 KB]
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image-20220613151413943.png [1.1 MB]
📁 📁 img2
image-20231101114450785.png [843.2 KB]
fasttext纪要.md [8.7 KB]
glue表格.xlsx [11.2 KB]
glue任务识别表.png [813.1 KB]
📁 📁 day11_bert模型简介和总结
📁 📁 3.代码
📁 📁 课堂代码
dm04_bert模型动静词向量.py [2.7 KB]
📁 📁 glue_data
📁 📁 QQP
📁 📁 original
quora_duplicate_questions.tsv [55.5 MB]
train.tsv [49.9 MB]
📁 📁 MNLI
📁 📁 original
multinli_1.0_dev_mismatched.jsonl [12.8 MB]
multinli_1.0_dev_matched.jsonl [12.3 MB]
multinli_1.0_train.txt [389.8 MB]
multinli_1.0_train.jsonl [469.6 MB]
multinli_1.0_dev_mismatched.txt [10.7 MB]
multinli_1.0_dev_matched.txt [10.1 MB]
dev_mismatched.tsv [10.5 MB]
dev_matched.tsv [10.0 MB]
test_mismatched.tsv [9.9 MB]
test_matched.tsv [9.4 MB]
README.txt [1.1 KB]
train.tsv [390.8 MB]
📁 📁 SST-2
📁 📁 original
original_rt_snippets.txt [1.1 MB]
sentiment_labels.txt [3.1 MB]
SOStr.txt [1.2 MB]
dictionary.txt [11.5 MB]
datasetSentences.txt [1.2 MB]
datasetSplit.txt [81.8 KB]
README.txt [2.3 KB]
STree.txt [1.2 MB]
cached_dev_BertTokenizer_128_sst-2 [723.4 KB]
train.tsv [3.6 MB]
test.tsv [192.7 KB]
cached_train_BertTokenizer_128_sst-2 [53.7 MB]
dev.tsv [92.7 KB]
📁 📁 SNLI
📁 📁 original
snli_1.0_test.jsonl [9.3 MB]
snli_1.0_train.txt [358.3 MB]
snli_1.0_dev.jsonl [9.3 MB]
snli_1.0_test.txt [7.2 MB]
snli_1.0_train.jsonl [464.9 MB]
snli_1.0_dev.txt [7.2 MB]
dev.tsv [7.1 MB]
test.tsv [7.1 MB]
train.tsv [359.3 MB]
README.txt [5.7 KB]
📁 📁 MRPC
test.tsv [434.9 KB]
cached_dev_BertTokenizer_128_mrpc [346.3 KB]
cached_train_BertTokenizer_128_mrpc [3.0 MB]
msr_paraphrase_train.txt [1022.5 KB]
msr_paraphrase_test.txt [430.9 KB]
dev.tsv [103.1 KB]
dev_ids.tsv [6.1 KB]
📁 📁 STS-B
📁 📁 original
sts-train.tsv [880.1 KB]
sts-dev.tsv [249.7 KB]
sts-test.tsv [275.8 KB]
LICENSE.txt [6.5 KB]
readme.txt [5.9 KB]
dev.tsv [270.4 KB]
test.tsv [285.7 KB]
train.tsv [961.0 KB]
📁 📁 CoLA
📁 📁 original
📁 📁 tokenized
in_domain_dev.tsv [26.0 KB]
in_domain_train.tsv [428.4 KB]
out_of_domain_dev.tsv [27.8 KB]
📁 📁 raw
in_domain_train.tsv [418.5 KB]
in_domain_dev.tsv [25.3 KB]
out_of_domain_dev.tsv [27.1 KB]
dev.tsv [52.5 KB]
test.tsv [47.6 KB]
train.tsv [418.5 KB]
📁 📁 5.作业
📁 📁 6.今日总结
04-英译法seq2seq架构.xmind [177.2 KB]
02-RNN及其变体知识体系梳理.xmind [5.3 MB]
06-fasttext与迁移学习知识体系梳理.xmind [6.4 MB]
07-BERT模型相关.xmind [2.6 MB]
重点复习-v2.md [1.7 KB]
03-人名分类器 实现分析.xmind [946.6 KB]
01-文本预处理知识体系梳理.xmind [2.8 MB]
05-transformer知识体系梳理.xmind [3.2 MB]
📁 📁 1.讲义
07-案例Seq2Seq英译法案例.pdf [2.6 MB]
11-迁移学习-下.pdf [1.4 MB]
03-文本预处理-下.pdf [1.5 MB]
04-RNN及其变体.pdf [3.0 MB]
09-迁移学习-上.pdf [3.3 MB]
08-Tranformer架构和实现.pdf [7.3 MB]
05-RNN案例人名分类器.pdf [3.0 MB]
06-注意力机制介绍.pdf [1.7 MB]
02-文本预处理-上.pdf [2.1 MB]
12-BERT系列模型-v2.pdf [7.7 MB]
10-迁移学习-中.pdf [3.4 MB]
01-自然语言处理概念.pdf [1.2 MB]
📁 📁 2.笔记
📁 阶段11-红蜘蛛知识图谱项目
📁 📁 day08
8-优化功能总结.mp4 [55.7 MB]
4-红蜘蛛课程回顾2.mp4 [49.4 MB]
6-RE模型重点复习.mp4 [81.6 MB]
10-面试题总结1.mp4 [213.8 MB]
5-NER模型重点复习.mp4 [70.0 MB]
7-在线部分子功能模块复习.mp4 [153.0 MB]
1-大厂主流预训练模型介绍.mp4 [49.0 MB]
3-红蜘蛛课程回顾1.mp4 [59.5 MB]
2-基础知识复习.mp4 [62.1 MB]
9-基础知识复习.mp4 [150.4 MB]
11-面试题总结2.mp4 [193.5 MB]
📁 📁 day01
day01_02_面试总结2_.mp4 [44.4 MB]
day01_08_RoBerta模型解析_.mp4 [21.3 MB]
day01_05_第2章_2.2小节_.mp4 [48.7 MB]
day01_04_第3小节_.mp4 [126.8 MB]
day01_01_面试总结1_.mp4 [87.7 MB]
day01_06_BERT源代码分析_.mp4 [68.2 MB]
day01_07_Albert模型解析_.mp4 [64.1 MB]
day01_09_MacBERT模型解析_.mp4 [25.2 MB]
day01_03_红蜘蛛第1章_第1-2节_.mp4 [81.0 MB]
📁 📁 day05视频_课堂笔记
图数据库写入数据1.mp4 [145.1 MB]
红蜘蛛上线实操.mp4 [44.1 MB]
问题分类子任务代码分析与实现.mp4 [129.5 MB]
问题解析子任务代码分析与实现.mp4 [54.0 MB]
答案查询子任务代码分析与实现.mp4 [17.0 MB]
课程回顾复习.mp4 [123.0 MB]
图数据库写入数据2.mp4 [40.9 MB]
📁 📁 day02
day02_03_工具类函数实现_.mp4 [67.4 MB]
day02_02_IDCNN模型介绍_.mp4 [33.5 MB]
day02_06_IDCNNCRF代码解析_.mp4 [34.5 MB]
day02_04_工具类代码实现_.mp4 [53.1 MB]
day02_08_3.2小节FLAT模型介绍_.mp4 [41.0 MB]
day02_07_IDCNNCRF代码实现_.mp4 [55.1 MB]
day02_01_NER问题解析_.mp4 [34.9 MB]
day02_09_3.3小节规则派NER_.mp4 [31.5 MB]
day02_05_IDCNN模型代码实现_.mp4 [32.4 MB]
📁 📁 day06视频_课堂笔记
GPT2优化版本V1.2详解.mp4 [124.3 MB]
9.1小节功能优化V1.1数据写入解析.mp4 [43.7 MB]
概念图谱.mp4 [95.7 MB]
知识图谱众包.mp4 [49.2 MB]
意图识别模型优化.mp4 [82.7 MB]
课程内容复习.mp4 [31.1 MB]
生成式模型优化V1.2版本详解.mp4 [55.1 MB]
意图识别模块优化升级.mp4 [25.0 MB]
📁 📁 day07视频_课堂笔记
1-意图识别优化_数据端.mp4 [87.5 MB]
3-意图识别优化_V2.0-part2.mp4 [70.6 MB]
4-ONNXRUNTIME加速实现.mp4 [122.4 MB]
5-经典知识补全.mp4 [39.8 MB]
6-错误知识发现与纠错介绍.mp4 [43.7 MB]
7-大厂商的一些预训练模型介绍.mp4 [71.4 MB]
2-意图识别优化_V2.0-part1.mp4 [37.2 MB]
📁 📁 day04
multi-head-selection模型详解2.mp4 [298.0 MB]
multi-head-selection模型详解1.mp4 [209.9 MB]
multi-head-selection模型详解3.mp4 [171.7 MB]
训练模型讲解.mp4 [110.1 MB]
事件抽取和schema解析.mp4 [106.7 MB]
📁 📁 day03
day03_06_KG-BERT模型详解_.mp4 [23.4 MB]
day03_02_XLNet模型详解2_.mp4 [40.8 MB]
day03_01_XLNet模型详解1_.mp4 [27.3 MB]
day03_03_Electra模型详解_.mp4 [52.3 MB]
day03_04_FinBERT模型详解_.mp4 [24.4 MB]
day03_05_K-BERT模型详解_.mp4 [54.6 MB]
📁 阶段8-AI医疗项目实战
📁 📁 day06
09 在线部分准备工作-花生壳_(5399133).mp4 [33.1 MB]
11 配置问题答疑_(5623955).mp4 [45.9 MB]
02 模型评估代码分析_(7411301).mp4 [99.4 MB]
07 NER模型小结_(0443215).mp4 [25.1 MB]
13 主要逻辑流程分析1_(1351049).mp4 [38.7 MB]
01 昨日复习_(8104149).mp4 [121.1 MB]
06 模型使用代码实现_(6342919).mp4 [53.1 MB]
12 werobot代码流程分析_(1011941).mp4 [40.6 MB]
14 主要逻辑流程分析2_(5750835).mp4 [60.9 MB]
08 上午复习_(9488106).mp4 [33.6 MB]
05 模型使用代码分析_(9438563).mp4 [37.0 MB]
03 模型评估代码实现1_(3268182).mp4 [143.7 MB]
04 模型评估代码实现2_(1138482).mp4 [45.5 MB]
📁 📁 day04
02 作业说明_(8164082).mp4 [20.3 MB]
01 昨日复习_(8164082).mp4 [260.8 MB]
12 BiLSTM代码实现_(3779833).mp4 [109.6 MB]
07 单条路径分数计算_(1416811).mp4 [56.1 MB]
09 单条路径分数计算代码_(5647234).mp4 [63.5 MB]
10 全部路径分数计算代码_(8680356).mp4 [60.9 MB]
04 BiLSTM模型介绍_(8499999).mp4 [40.6 MB]
13 CRF代码实现1_(5325449).mp4 [131.2 MB]
08 全部路径分数计算_(0760198).mp4 [82.8 MB]
05 BiLSTM+CRF模型介绍_(7935387).mp4 [29.1 MB]
03 CRF模型介绍_(8164082).mp4 [40.5 MB]
11 上午复习_(3356353).mp4 [237.8 MB]
06 模型损失函数构建_(8064745).mp4 [70.9 MB]
📁 📁 day07
01 昨日复习_(4190714).mp4 [104.8 MB]
05 项目配置联调_(6103261).mp4 [121.4 MB]
07 项目部署常见问题_(9747790).mp4 [195.7 MB]
06 项目部署调试_(5485435).mp4 [103.8 MB]
03 句子主题相关模型介绍2_(6943141).mp4 [74.5 MB]
02 句子主题相关模型介绍_(8548491).mp4 [55.2 MB]
04 句子主题相关模型部署_(1368175).mp4 [65.2 MB]
📁 📁 模型部署
06-模型训练-邮件模型训练_(8534369).mp4 [16.5 MB]
05-模型训练-文本特征提取_(3277562).mp4 [61.2 MB]
01-模型部署内容概述_(1250523).mp4 [11.1 MB]
04-模型训练-邮件数据清洗_(6517510).mp4 [50.9 MB]
18-容器部署-镜像操作_(3063280).mp4 [49.7 MB]
12-服务接口-创建表单_(9626275).mp4 [67.0 MB]
03-模型训练-数据格式转换_(1516600).mp4 [64.2 MB]
08-模型训练-邮件模型预测_(0939187).mp4 [40.1 MB]
15-服务接口-服务封装_(5454438).mp4 [90.8 MB]
17-容器部署-快速入门_(4622889).mp4 [79.7 MB]
13-服务接口-处理表单_(5842378).mp4 [31.7 MB]
09-服务接口-Flask作用_(9314294).mp4 [26.8 MB]
21-容器部署-自动构建镜像_(2193961).mp4 [61.4 MB]
07-模型训练-邮件模型评估_(1496087).mp4 [76.9 MB]
22-模型部署回顾_(9326453).mp4 [31.8 MB]
11-服务接口-Flask-Hello-World-2_(8121060).mp4 [34.2 MB]
10-服务接口-Flask-Hello-World-1_(3796055).mp4 [31.9 MB]
20-容器部署-手动构建镜像_(3263228).mp4 [98.0 MB]
14-服务接口-表单扩展_(0160441).mp4 [37.1 MB]
02-模型训练-数据集介绍_(5898757).mp4 [20.7 MB]
16-容器部署-介绍安装_(4812390).mp4 [55.9 MB]
19-容器部署-容器操作_(4708301).mp4 [31.7 MB]
📁 📁 day03
day03-18 内容串讲_.mp4 [82.8 MB]
day03-21 作业说明_.mp4 [20.9 MB]
day03-04 统计语言模型介绍_.mp4 [62.1 MB]
day03-20 分词指标介绍_.mp4 [35.3 MB]
day03-15 维特比算法分析_.mp4 [21.8 MB]
day03-03 序列标注问题介绍_.mp4 [70.2 MB]
day03-12 隐马模型训练代码分析1_.mp4 [81.2 MB]
day03-08 前向概率算法_.mp4 [74.9 MB]
day03-02 昨日复习_.mp4 [37.4 MB]
day03-01 作业点评_.mp4 [20.7 MB]
day03-13 隐马模型训练代码分析2_.mp4 [35.9 MB]
day03-09 前向概率代码_.mp4 [25.1 MB]
day03-16 维特比算法示例_.mp4 [32.1 MB]
day03-06 隐马尔可夫模型介绍_.mp4 [58.7 MB]
day03-14 维特比算法介绍_.mp4 [65.3 MB]
day03-19 分词代码分析_.mp4 [50.1 MB]
day03-07 球和盒子模型介绍_.mp4 [61.5 MB]
day03-10 分词案例背景介绍_.mp4 [72.0 MB]
day03-17 维特比算法代码实现_.mp4 [59.5 MB]
day03-05 马尔可夫模型介绍_.mp4 [22.1 MB]
day03-11 上午复习_.mp4 [56.8 MB]
📁 📁 day05
03 昨日复习_(9040710).mp4 [139.2 MB]
11 数据预处理流程分析_(7821661).mp4 [70.0 MB]
10 NER模型代码实现_(6111430).mp4 [42.5 MB]
05 课间答疑矩阵计算问题_(3406790).mp4 [17.9 MB]
07 课间答疑预测函数问题_(3811715).mp4 [30.3 MB]
15 模型训练流程分析_(2746966).mp4 [71.8 MB]
01 作业点评1_(2314179).mp4 [49.7 MB]
14 数据集转换成dataset_(0556323).mp4 [100.1 MB]
16 main函数实现_(6504762).mp4 [51.8 MB]
17 start_train函数实现_(6890088).mp4 [27.2 MB]
12 准备tokenizer字符_(1945068).mp4 [19.0 MB]
18 pad_batch_inputs函数实现_(9671859).mp4 [77.1 MB]
02 作业点评2_(0999938).mp4 [95.1 MB]
09 上午复习_(6248193).mp4 [41.9 MB]
08 预测函数代码实现_(7198969).mp4 [75.7 MB]
13 处理原始数据集_(8468437).mp4 [38.3 MB]
04 全部路径分数代码实现_(1828110).mp4 [86.9 MB]
📁 📁 day02
day02-01 作业点评_.mp4 [17.9 MB]
day02-14 RNN模型介绍_.mp4 [19.1 MB]
day02-02 昨日复习_.mp4 [114.2 MB]
day02-16 RNN模型代码实现_.mp4 [37.1 MB]
day02-07 课间答疑_.mp4 [25.7 MB]
day02-23 模型使用代码实现_.mp4 [55.7 MB]
day02-22 模型使用介绍_.mp4 [37.0 MB]
day02-19 训练函数代码实现_.mp4 [49.2 MB]
day02-08 非结构化数据流程介绍_.mp4 [13.5 MB]
day02-12 中文预训练模型介绍_.mp4 [39.7 MB]
day02-18 训练函数介绍_.mp4 [21.7 MB]
day02-13 预训练模型代码实现_.mp4 [23.9 MB]
day02-20 主函数流程介绍_.mp4 [24.6 MB]
day02-04 结构化数据流程介绍_.mp4 [84.0 MB]
day02-15 课间答疑_.mp4 [11.9 MB]
day02-06 结构化数据写入neo4j2_.mp4 [48.6 MB]
day02-17 随机取样函数实现_.mp4 [21.3 MB]
day02-09 命名实体审核任务介绍_.mp4 [20.5 MB]
day02-05 结构化数据写入neo4j1_.mp4 [35.5 MB]
day02-10 命名实体审核数据查看_.mp4 [7.8 MB]
day02-21 主函数代码实现_.mp4 [67.5 MB]
day02-03 离线部分介绍_.mp4 [23.6 MB]
day02-11 上午复习_.mp4 [30.9 MB]
📁 📁 day01
day01-25 python使用neo4j_.mp4 [28.2 MB]
day01-14 上午复习2_.mp4 [49.0 MB]
day01-06 工具介绍-flask介绍_.mp4 [31.5 MB]
day01-10 工具介绍-redis代码_.mp4 [15.1 MB]
day01-15 neo4j介绍_.mp4 [27.8 MB]
day01-24 Cypher语法介绍-创建删除索引_.mp4 [4.6 MB]
day01-03 工具介绍-unit api介绍_.mp4 [42.4 MB]
day01-20 Cypher语法介绍-查询删除排序_.mp4 [28.4 MB]
day01-22 Cypher语法介绍-聚合函数_.mp4 [9.7 MB]
day01-09 课件答疑_.mp4 [22.0 MB]
day01-17 supervisor管理neo4j_.mp4 [16.1 MB]
day01-19 Cypher语法介绍-创建关系_.mp4 [13.9 MB]
day01-02 AI医生背景介绍2_.mp4 [13.1 MB]
day01-01 AI医生背景介绍_.mp4 [49.9 MB]
day01-23 索引介绍_.mp4 [10.6 MB]
day01-05 工具介绍-unit代码运行_.mp4 [10.2 MB]
day01-07 工具介绍-gunicorn_.mp4 [22.3 MB]
day01-18 Cypher语法介绍-创建节点_.mp4 [29.3 MB]
day01-21 Cypher语法介绍-字符串_.mp4 [18.4 MB]
day01-08 工具介绍-redis介绍_.mp4 [28.4 MB]
day01-16 neo4j安装_.mp4 [40.2 MB]
day01-13 上午复习1_.mp4 [32.2 MB]
day01-12 工具介绍-supervisor配置_.mp4 [18.3 MB]
day01-11 工具介绍-supervisor_.mp4 [25.7 MB]
day01-04 远程连接虚拟机_.mp4 [14.9 MB]
day01-26 neo4j事务_.mp4 [23.4 MB]
📁 阶段6-深度学习基础
📁 📁 02-神经网络
08-softmax_.mp4 [58.4 MB]
02-神经网络构成_.mp4 [10.4 MB]
25.RMSprop_.mp4 [27.5 MB]
18-梯度下降算法_.mp4 [37.2 MB]
21.SGD的问题_.mp4 [15.4 MB]
22-指数加权平均_.mp4 [51.7 MB]
15.交叉熵_.mp4 [67.7 MB]
17.回归损失_.mp4 [44.6 MB]
28.内容回顾_.mp4 [41.6 MB]
05-sigmoid_.mp4 [38.7 MB]
16.二分类交叉熵_.mp4 [18.8 MB]
26.等间隔学习率衰减_.mp4 [38.1 MB]
14.内容回顾_.mp4 [28.8 MB]
07-relu_.mp4 [30.2 MB]
30.BN层_.mp4 [16.2 MB]
13-参数量统计_.mp4 [24.9 MB]
32.案例1_.mp4 [71.7 MB]
33.训练_.mp4 [44.5 MB]
19-反向传播过程_.mp4 [58.5 MB]
09-参数初始化1_.mp4 [34.5 MB]
29.正则化_.mp4 [46.7 MB]
06-tanh_.mp4 [23.2 MB]
03-神经网络参数和超参数_.mp4 [7.3 MB]
04-激活函数的作用_.mp4 [34.7 MB]
27-学习率衰减_.mp4 [18.9 MB]
12-神经网络构建_.mp4 [63.7 MB]
34.预测评估_.mp4 [45.3 MB]
23-动量法_.mp4 [32.2 MB]
10-参数初始化_.mp4 [25.4 MB]
01-神经网络介绍_.mp4 [61.2 MB]
20-反向传播实现_.mp4 [64.7 MB]
24-adagrad_.mp4 [16.6 MB]
📁 📁 01-pytorch框架
21-cat_.mp4 [23.5 MB]
18-squeese_.mp4 [23.7 MB]
14-布尔索引_.mp4 [25.7 MB]
17-内容回顾_.mp4 [3.1 MB]
15-多维索引_.mp4 [26.1 MB]
07-张量的类型转换_.mp4 [40.2 MB]
24-pytorch框架总结_.mp4 [18.7 MB]
16-形状操作_.mp4 [27.9 MB]
06-张量元素的类型转换_.mp4 [26.9 MB]
20-view_.mp4 [30.4 MB]
13-索引操作_.mp4 [31.9 MB]
08-item方法_.mp4 [15.2 MB]
04-线性张量和随机张量的创建_.mp4 [27.3 MB]
23-案例介绍1_.mp4 [88.9 MB]
05-全0 ，1 张量的创建_.mp4 [15.7 MB]
02.GPU版本安装_.mp4 [15.5 MB]
11-张量的数值计算_.mp4 [45.0 MB]
10-内容回顾_.mp4 [23.6 MB]
01.pytorch简介_.mp4 [47.1 MB]
22-自动微分模块_.mp4 [75.6 MB]
25.案例介绍2_.mp4 [82.2 MB]
19-transpose和permute_.mp4 [27.8 MB]
03-张量的创建_.mp4 [58.8 MB]
12-张量运算_.mp4 [34.9 MB]
09-内容总结_.mp4 [29.2 MB]
📁 📁 04-RNN
02-词嵌入介绍_.mp4 [52.8 MB]
06-数据加载_.mp4 [43.7 MB]
03-RNN的思想_.mp4 [47.7 MB]
总结_.mp4 [75.5 MB]
01-自然语言简介_.mp4 [34.8 MB]
04-RNN实践_.mp4 [14.2 MB]
08-模型预测_.mp4 [17.7 MB]
05-词表生成_.mp4 [97.0 MB]
07-模型构建与训练_.mp4 [51.6 MB]
📁 📁 00-深度学习简介
01.课程介绍_.mp4 [33.2 MB]
02.深度学习简介1_.mp4 [36.3 MB]
03.深度学习简介2_.mp4 [40.1 MB]
📁 📁 03-CNN
07-模型构建_.mp4 [50.9 MB]
08-内容总结_.mp4 [15.2 MB]
05-池化层_.mp4 [31.9 MB]
03-卷积的计算_.mp4 [41.7 MB]
06-数据获取_.mp4 [35.8 MB]
04-卷积实现_.mp4 [50.2 MB]
01-图像的基础知识_.mp4 [40.8 MB]
09-模型训练与评估_.mp4 [59.7 MB]
02-卷积神经网络的构成_.mp4 [24.2 MB]适合人群
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