![WM035 – 深度学习与PyTorch入门实战 [4.8G]](https://www.aizyt88.com/wp-content/themes/ripro-v2/assets/img/thumb.jpg)
目录里课时编号有些不太对 不过整个视频是完整连贯的 懒得改了
WM035 – 深度学习与PyTorch入门实战 [4.8G]
┃ ┣━━1.深度学习框架介绍 [48.7M]
┃ ┃ ┗━━1.lesson1-PyTorch介绍.mp4 [48.7M]
┃ ┣━━2.开发环境准备 [54.5M]
┃ ┃ ┗━━2.lesson2-开发环境准备.mp4 [54.5M]
┃ ┣━━3.初见深度学习 [208.6M]
┃ ┃ ┣━━3.lesson3-初探Linear Regression案例-1.mp4 [71.9M]
┃ ┃ ┣━━4.lesson3-初探Linear Regression案例-2.mp4 [43.1M]
┃ ┃ ┣━━5.lesson4-PyTorch求解Linear Regression案例.mp4 [35.7M]
┃ ┃ ┣━━6.lesson5 -手写数字问题引入1.mp4 [36.7M]
┃ ┃ ┗━━7.lesson5 -手写数字问题引入2.mp4 [21M]
┃ ┣━━4.Pytorch张量操作 [426.4M]
┃ ┃ ┣━━8.lesson6 基本数据类型1.mp4 [54.4M]
┃ ┃ ┣━━9.lesson6 基本数据类型2.mp4 [28.2M]
┃ ┃ ┣━━10.lesson7 创建Tensor 1.mp4 [51.6M]
┃ ┃ ┣━━11.lesson7 创建Tensor 2.mp4 [44.3M]
┃ ┃ ┣━━12.lesson8 索引与切片1.mp4 [47.2M]
┃ ┃ ┣━━13.lesson8 索引与切片2.mp4 [45.4M]
┃ ┃ ┣━━14.lesson9 维度变换1.mp4 [33.1M]
┃ ┃ ┣━━15.lesson9 维度变换2.mp4 [40.7M]
┃ ┃ ┣━━16.lesson9 维度变换3.mp4 [40.8M]
┃ ┃ ┗━━17.lesson9 维度变换4.mp4 [40.8M]
┃ ┣━━5.张量高阶操作 [405.3M]
┃ ┃ ┣━━18.lesson10 Broatcasting 1.mp4 [57.9M]
┃ ┃ ┣━━19.lesson10 Broatcasting 2.mp4 [46.2M]
┃ ┃ ┣━━20.lesson11 合并与切割1.mp4 [46.8M]
┃ ┃ ┣━━21.lesson11 合并与切割2.mp4 [30.8M]
┃ ┃ ┣━━22.lesson12 基本运算.mp4 [67.1M]
┃ ┃ ┣━━23.lesson13 数据统计1.mp4 [39.9M]
┃ ┃ ┣━━24.lesson13 数据统计2.mp4 [54.7M]
┃ ┃ ┗━━25.lesson14 高阶OP.mp4 [61.9M]
┃ ┣━━6.随机梯度下降 [286.1M]
┃ ┃ ┣━━26.lesson16 什么是梯度1.mp4 [69.2M]
┃ ┃ ┣━━27.lesson16 什么是梯度2.mp4 [43.3M]
┃ ┃ ┣━━28.lesson17 常见梯度.mp4 [18.4M]
┃ ┃ ┣━━29.lesson18 激活函数及其梯度1.mp4 [45.5M]
┃ ┃ ┣━━30.lesson18 激活函数及其梯度2.mp4 [44.4M]
┃ ┃ ┗━━31.lesson18 激活函数及其梯度3.mp4 [65.3M]
┃ ┣━━7.感知机梯度传播推导 [258.3M]
┃ ┃ ┣━━32.lesson19 单一输出感知机1.mp4 [47.4M]
┃ ┃ ┣━━33.lesson19 多输出Loss层2.mp4 [49.7M]
┃ ┃ ┣━━34.lesson20 链式法则.mp4 [39.9M]
┃ ┃ ┣━━35.lesson21 反向传播.mp4 [82M]
┃ ┃ ┗━━36.lesson22 优化小实例.mp4 [39.2M]
┃ ┣━━8.多层感知机与分类器 [353.9M]
┃ ┃ ┣━━37.lesson24 Logistic Regression.mp4 [47.8M]
┃ ┃ ┣━━38.lesson25 交叉熵.mp4 [72.8M]
┃ ┃ ┣━━39.lesson26 多分类实战.mp4 [35M]
┃ ┃ ┣━━40.lesson27 全连接层.mp4 [52.1M]
┃ ┃ ┣━━41.lesson28 激活函数与GPU加速.mp4 [39.6M]
┃ ┃ ┣━━42.lesson29 测试.mp4 [53.8M]
┃ ┃ ┗━━43.lesson30-Visdom可视化.mp4 [52.8M]
┃ ┣━━9.过拟合 [262.5M]
┃ ┃ ┣━━44.lesson31-过拟合与欠拟合.mp4 [42.5M]
┃ ┃ ┣━━45.lesson32-Train-Val-Test-交叉验证-1.mp4 [45.9M]
┃ ┃ ┣━━46.lesson32-Train-Val-Test-交叉验证-2.mp4 [32.3M]
┃ ┃ ┣━━47.lesson33-regularization.mp4 [39M]
┃ ┃ ┣━━48.lesson34-动量与lr衰减.mp4 [51.5M]
┃ ┃ ┗━━49.lesson35-early stopping, dropout, sgd.mp4 [51.2M]
┃ ┣━━10.卷积神经网络CNN [678.5M]
┃ ┃ ┣━━50.lesson37-什么是卷积-1.mp4 [62.8M]
┃ ┃ ┣━━51.lesson37-什么是卷积-2.mp4 [39.6M]
┃ ┃ ┣━━52.lesson38-卷积神经网络-1.mp4 [41.4M]
┃ ┃ ┣━━53.lesson38-卷积神经网络-2.mp4 [62.9M]
┃ ┃ ┣━━54.lesson38-卷积神经网络-3.mp4 [35.5M]
┃ ┃ ┣━━55.lesson39-Pooling&upsample.mp4 [34.1M]
┃ ┃ ┣━━56.lesson40-BatchNorm-1.mp4 [41.4M]
┃ ┃ ┣━━57.lesson40-BatchNorm-2.mp4 [51.3M]
┃ ┃ ┣━━58.lesson41-LeNet5,AlexNet, VGG, GoogLeN.mp4 [49.3M]
┃ ┃ ┣━━59.lesson41-LeNet5,AlexNet, VGG, GoogLeN.mp4 [40.4M]
┃ ┃ ┣━━60.lesson42-ResNet，DenseNet-1.mp4 [53.2M]
┃ ┃ ┣━━61.lesson42-ResNet, DenseNet-2.mp4 [43.6M]
┃ ┃ ┣━━62.lesson43-nn.Module-1.mp4 [45M]
┃ ┃ ┣━━63.lesson43-nn.Module-2.mp4 [31.4M]
┃ ┃ ┗━━64.lesson44-数据增强Data Argumentation.mp4 [46.8M]
┃ ┣━━11.CIFAR10与ResNet实战 [142.4M]
┃ ┃ ┣━━065 CIFAR10数据集介绍.mp4 [17.8M]
┃ ┃ ┣━━066 卷积神经网络实现-1.mp4 [22M]
┃ ┃ ┣━━067 卷积神经网络实现-2.mp4 [18.8M]
┃ ┃ ┣━━068 网络的训练.mp4 [16.9M]
┃ ┃ ┣━━069 网络的测试.mp4 [15M]
┃ ┃ ┣━━070 ResNet18网络.mp4 [18.5M]
┃ ┃ ┣━━071 ResNet实战.mp4 [13.7M]
┃ ┃ ┗━━072 实战小结.mp4 [19.6M]
┃ ┣━━12.迁移学习-实战宝可梦精灵 [268.3M]
┃ ┃ ┣━━课时100 自定义数据集实战-3.mp4 [16.6M]
┃ ┃ ┣━━课时101 自定义数据集实战-4.mp4 [15.1M]
┃ ┃ ┣━━课时102 自定义数据集实战-5.mp4 [21M]
┃ ┃ ┣━━课时103 自定义网络.mp4 [14.5M]
┃ ┃ ┣━━课时104 自定义网络训练与测试.mp4 [11.5M]
┃ ┃ ┣━━课时105 自定义网络实战.mp4 [54.5M]
┃ ┃ ┣━━课时106 迁移学习.mp4 [7.7M]
┃ ┃ ┣━━课时107 迁移学习实战.mp4 [15.8M]
┃ ┃ ┣━━课时96 Pokemon数据集.mp4 [47.9M]
┃ ┃ ┣━━课时97 数据预处理.mp4 [17.8M]
┃ ┃ ┣━━课时98 自定义数据集实战-1.mp4 [31.8M]
┃ ┃ ┗━━课时99 自定义数据集实战-2.mp4 [14.1M]
┃ ┣━━12.循环神经网络RNN&LSTM [465M]
┃ ┃ ┣━━65.lesson46-时间序列表示.mp4 [53.5M]
┃ ┃ ┣━━66.lesson47-RNN原理-1.mp4 [28.4M]
┃ ┃ ┣━━67.lesson47-RNN原理-2.mp4 [34.9M]
┃ ┃ ┣━━68.lesson48-RNN Layer使用-1.mp4 [34.2M]
┃ ┃ ┣━━69.lesson48-RNN Layer使用-2.mp4 [29.9M]
┃ ┃ ┣━━70.lesson49-时间序列预测.mp4 [53.3M]
┃ ┃ ┣━━71.lesson50-RNN训练难题.mp4 [55M]
┃ ┃ ┣━━72.lesson51-LSTM原理-1.mp4 [33M]
┃ ┃ ┣━━73.lesson51-LSTM原理-2.mp4 [45.7M]
┃ ┃ ┣━━74.lesson52-LSTM Layer使用.mp4 [28.4M]
┃ ┃ ┗━━75.lesson53-情感分类实战.mp4 [68.6M]
┃ ┣━━13.自编码器Auto-Encoders [325.1M]
┃ ┃ ┣━━课时108 无监督学习.mp4 [58.5M]
┃ ┃ ┣━━课时109 Auto-Encoder原理.mp4 [17.4M]
┃ ┃ ┣━━课时110 Auto-Encoder变种.mp4 [14M]
┃ ┃ ┣━━课时111 Adversarial Auto-Encoder.mp4 [13.2M]
┃ ┃ ┣━━课时112 变分Auto-Encoder引入.mp4 [65.2M]
┃ ┃ ┣━━课时113 Reparameterization trick.mp4 [12.8M]
┃ ┃ ┣━━课时114 变分自编码器VAE.mp4 [17.7M]
┃ ┃ ┣━━课时115 Auto-Encoder实战-1.mp4 [16M]
┃ ┃ ┣━━课时116 Auto-Encoder实战-2.mp4 [43.2M]
┃ ┃ ┣━━课时117 变分Auto-Encoder实战-1.mp4 [27.7M]
┃ ┃ ┗━━课时118 变分Auto-Encoder实战-2.mp4 [39.5M]
┃ ┣━━14.对抗生成网络GAN [316.2M]
┃ ┃ ┣━━76.lesson54-数据分布.mp4 [17.4M]
┃ ┃ ┣━━77.lesson55-画家的成长历程.mp4 [28.9M]
┃ ┃ ┣━━78.lesson56-GAN发展.mp4 [23M]
┃ ┃ ┣━━79.lesson57-纳什均衡-D.mp4 [20.4M]
┃ ┃ ┣━━80.lesson58-纳什均衡-G.mp4 [36.6M]
┃ ┃ ┣━━81.lesson59-JS散度的弊端.mp4 [36.8M]
┃ ┃ ┣━━82.lesson60-EM距离.mp4 [17.2M]
┃ ┃ ┣━━83.lesson61-WGAN与WGAN-GP.mp4 [28.8M]
┃ ┃ ┣━━84.lesson62-G和D实现.mp4 [17.3M]
┃ ┃ ┣━━85.lesson63-GAN实战.mp4 [33.3M]
┃ ┃ ┣━━86.lesson64-GAN训练不稳定.mp4 [20.2M]
┃ ┃ ┗━━87.lesson65-WGAN-GP实战.mp4 [36.3M]
┃ ┣━━15.【选看】Ubuntu开发环境安装.rar [113.5M]
┃ ┣━━16.【选看】人工智能发展简史.rar [104.4M]
┃ ┣━━17.【选看】Numpy实战BP神经网络.rar [120.3M]
┃ ┗━━深度学习与Pytorch-PPT和代码.rar [81.2M]