2024python深度学习框架pytorch

python深度学习框架pytorch

*   01 1.pytorch简介.mp4 (11.66 MB), 04:16

*   02 2.pytorch-GPU加速.mp4 (12.93 MB), 04:56

*   03 3pytorch-自动求导体验.mp4 (5.93 MB), 02:23

*   04 4.python安装.mp4 (5.99 MB), 01:55

*   05 5.安装jupyter.mp4 (5.74 MB), 01:11

*   06 6.配置pycharm.mp4 (11.15 MB), 01:56

*   07 7.配置cuda11.1.mp4 (24.53 MB), 05:49

*   08 8.安装pytorch.mp4 (43.86 MB), 12:25

*   09 9.配置cudnn与pytorch-CUDA加速.mp4 (9.43 MB), 02:55

*   10 10.测试CUDA与pycharm中文.mp4 (14.45 MB), 02:53

*   11 11线性回归.mp4 (30.67 MB), 09:37

*   12 12.线性回归损失函数.mp4 (18.80 MB), 04:43

*   13 13.线性回归梯度计算.mp4 (18.73 MB), 05:23

*   14 14线性回归测试.mp4 (19.38 MB), 05:10

*   15 15mnist概述.mp4 (9.34 MB), 02:42

*   16 16mnist实践上数据处理.mp4 (16.29 MB), 05:33

*   17 17mnist实践中数据训练巡视函数趋势.mp4 (26.83 MB), 08:33

*   18 18mnist实践下计算识别率.mp4 (24.33 MB), 06:50

*   19 19.pytorch基本类型-类型转换-类型判断.mp4 (23.33 MB), 08:02

*   20 20.pytorch基本数据类型-向量.mp4 (16.62 MB), 07:08

*   21 21pytorch基本数据类型高维向量.mp4 (10.18 MB), 03:18

*   22 22pytorch创建tensor上.mp4 (44.93 MB), 17:42

*   23 23.pytorch创建tensor下.mp4 (11.96 MB), 04:19

*   24 24pytorch索引切片上.mp4 (34.43 MB), 13:21

*   25 25.pytorch索引与切片下.mp4 (10.31 MB), 03:31

*   26 26pytorch降维与升维.mp4 (39.52 MB), 14:16

*   27 27pytorch维度变换高级.mp4 (30.91 MB), 10:45

*   28 28.pytorch-Broadcasting自动拓展.mp4 (21.59 MB), 07:27

*   29 29.pytorch-拼接-cat.mp4 (23.32 MB), 07:07

*   30 30.pytorch拼接stack.mp4 (14.83 MB), 05:04

*   31 31pytorch拆分.mp4 (15.78 MB), 04:38

*   32 32pytorch基本计算上.mp4 (35.02 MB), 13:21

*   33 33pytorch基本计算下.mp4 (20.67 MB), 08:28

*   34 34pytorch数据统计.mp4 (55.34 MB), 19:47

*   35 35pytorch高阶操作.mp4 (20.22 MB), 07:08

*   36 36梯度概念.mp4 (12.58 MB), 03:43

*   37 37常见函数梯度.mp4 (4.28 MB), 01:20

*   38 38激活函数与梯度.mp4 (19.16 MB), 06:24

*   39 39MSE-loss函数与梯度.mp4 (30.15 MB), 09:06

*   40 40softmax损失函数与梯度.mp4 (17.92 MB), 05:27

*   41 41.感知机初级.mp4 (17.96 MB), 06:26

*   42 42.感知机高级-多输出.mp4 (17.41 MB), 04:46

*   43 43pytorch链式法则.mp4 (23.99 MB), 08:54

*   44 44pytorch反向传播原理.mp4 (23.49 MB), 05:18

*   45 45pytorch反向传播实践.mp4 (52.21 MB), 12:18

*   46 46Pytorch-2D函数优化实例.mp4 (31.89 MB), 09:53

*   47 47逻辑回归概述.mp4 (17.57 MB), 03:57

*   48 48逻辑回归实战.mp4 (23.14 MB), 06:46

*   49 49信息熵.mp4 (16.20 MB), 05:13

*   50 50交叉熵.mp4 (37.59 MB), 09:55

*   51 51多分类.mp4 (65.48 MB), 19:40

*   52 52全链接层.mp4 (54.55 MB), 14:51

*   53 53损失函数.mp4 (30.51 MB), 09:06

*   54 54GPU加速.mp4 (12.30 MB), 04:15

*   55 55GPU加速mnist.mp4 (39.81 MB), 10:30

*   56 56GPU加速batch如何影响训练速度.mp4 (55.78 MB), 18:18

*   57 57.Loss与Accuracy.mp4 (5.78 MB), 01:29

*   58 59Visdom可视化.mp4 (53.81 MB), 16:16

*   59 60mnist可视化.mp4 (37.24 MB), 10:42

*   60 61.TensorboardX.mp4 (27.83 MB), 06:18

*   61 62.过拟合与欠拟合.mp4 (35.29 MB), 07:14

*   62 63K-fold验证实现防止过拟合.mp4 (51.11 MB), 13:06

*   63 64.正则化Regularization.mp4 (14.57 MB), 05:01

*   64 65.L2范式正则化实战.mp4 (40.02 MB), 09:49

*   65 66动量与学习率衰减.mp4 (12.67 MB), 04:03

*   66 67.L1正则化动量学习率衰减-mnist实战.mp4 (56.29 MB), 16:06

*   67 68.避免过拟合EarlyStop与Dropout.mp4 (10.99 MB), 03:07

*   68 69.实践避免过拟合EarlyStop与Dropout.mp4 (62.23 MB), 16:18

*   69 70卷积概念.mp4 (10.98 MB), 03:16

*   70 71卷积神经网络.mp4 (5.76 MB), 01:36

*   71 72实战卷积神经网络.mp4 (17.27 MB), 05:18

*   72 73Downupsample.mp4 (20.25 MB), 06:53

*   73 74batchNorm.mp4 (27.62 MB), 07:52

*   74 75经典卷积神经网络.mp4 (43.64 MB), 12:26

*   75 76深度残差网络ResNet.mp4 (35.98 MB), 11:46

*   76 77nn.Module.mp4 (35.33 MB), 11:05

*   77 78数据增强.mp4 (36.54 MB), 09:15

*   78 79cifar10图像数据预览.mp4 (31.28 MB), 09:19

*   79 80cifar10图像识别resnet.mp4 (23.87 MB), 07:52

*   80 81图像识别lenet5.mp4 (8.45 MB), 02:28

*   81 84时间序列.mp4 (52.93 MB), 17:35

*   82 85循环神经网络概述.mp4 (18.39 MB), 04:27

*   83 86时间序列预测.mp4 (41.20 MB), 11:59

*   84 87梯度爆炸与梯度消失以及梯度压缩.mp4 (20.53 MB), 05:45

*   85 88LSTM如何解决梯度消失以及实践.mp4 (27.79 MB), 08:42

*   86 89LSTM与单元使用.mp4 (10.23 MB), 02:34

*   87 90LSTM情感分析介绍.mp4 (20.94 MB), 05:18

*   88 91LSTM情感分析实战.mp4 (176.78 MB), 56:39

*   89 92AutoEncoder自动编码器原理.mp4 (20.02 MB), 06:49

*   90 93mnist编码器实战上.mp4 (34.57 MB), 10:03

*   91 94mnist自动编码器VAE实战.mp4 (45.27 MB), 13:51

*   92 95自动编码器AE实战.mp4 (11.38 MB), 03:49

*   93 96对抗神经网络原理.mp4 (20.92 MB), 04:35

*   94 99gan与wgan_gp差异.mp4 (11.76 MB), 02:05

*   95 100图卷积网络GCN.mp4 (38.96 MB), 07:00

*   96 101处理自定义数据.mp4 (74.01 MB), 15:55

*   97 课程资料.txt (0.00 MB)