目錄：
┣━━1.深度學習框架介紹 [48.7M]
┃ ┗━━1.lesson1-PyTorch介紹.mp4 [48.7M]
┣━━2.開發環境準備 [54.5M]
┃ ┗━━2.lesson2-開發環境準備.mp4 [54.5M]
┣━━3.初見深度學習 [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 -手寫數字問題引入1.mp4 [36.7M]
┃ ┗━━7.lesson5 -手寫數字問題引入2.mp4 [21M]
┣━━4.Pytorch張量操作 [426.4M]
┃ ┣━━8.lesson6 基本數據類型1.mp4 [54.4M]
┃ ┣━━9.lesson6 基本數據類型2.mp4 [28.2M]
┃ ┣━━10.lesson7 創建Tensor 1.mp4 [51.6M]
┃ ┣━━11.lesson7 創建Tensor 2.mp4 [44.3M]
┃ ┣━━12.lesson8 索引與切片1.mp4 [47.2M]
┃ ┣━━13.lesson8 索引與切片2.mp4 [45.4M]
┃ ┣━━14.lesson9 維度變換1.mp4 [33.1M]
┃ ┣━━15.lesson9 維度變換2.mp4 [40.7M]
┃ ┣━━16.lesson9 維度變換3.mp4 [40.8M]
┃ ┗━━17.lesson9 維度變換4.mp4 [40.8M]
┣━━5.張量高階操作 [405.3M]
┃ ┣━━18.lesson10 Broatcasting 1.mp4 [57.9M]
┃ ┣━━19.lesson10 Broatcasting 2.mp4 [46.2M]
┃ ┣━━20.lesson11 合併與切割1.mp4 [46.8M]
┃ ┣━━21.lesson11 合併與切割2.mp4 [30.8M]
┃ ┣━━22.lesson12 基本運算.mp4 [67.1M]
┃ ┣━━23.lesson13 數據統計1.mp4 [39.9M]
┃ ┣━━24.lesson13 數據統計2.mp4 [54.7M]
┃ ┗━━25.lesson14 高階OP.mp4 [61.9M]
┣━━6.隨機梯度下降 [286.1M]
┃ ┣━━26.lesson16 什麼是梯度1.mp4 [69.2M]
┃ ┣━━27.lesson16 什麼是梯度2.mp4 [43.3M]
┃ ┣━━28.lesson17 常見梯度.mp4 [18.4M]
┃ ┣━━29.lesson18 激活函數及其梯度1.mp4 [45.5M]
┃ ┣━━30.lesson18 激活函數及其梯度2.mp4 [44.4M]
┃ ┗━━31.lesson18 激活函數及其梯度3.mp4 [65.3M]
┣━━7.感知機梯度傳播推導 [258.3M]
┃ ┣━━32.lesson19 單一輸出感知機1.mp4 [47.4M]
┃ ┣━━33.lesson19 多輸出Loss層2.mp4 [49.7M]
┃ ┣━━34.lesson20 鏈式法則.mp4 [39.9M]
┃ ┣━━35.lesson21 反向傳播.mp4 [82M]
┃ ┗━━36.lesson22 優化小實例.mp4 [39.2M]
┣━━8.多層感知機與分類器 [353.9M]
┃ ┣━━37.lesson24 Logistic Regression.mp4 [47.8M]
┃ ┣━━38.lesson25 交叉熵.mp4 [72.8M]
┃ ┣━━39.lesson26 多分類實戰.mp4 [35M]
┃ ┣━━40.lesson27 全連接層.mp4 [52.1M]
┃ ┣━━41.lesson28 激活函數與GPU加速.mp4 [39.6M]
┃ ┣━━42.lesson29 測試.mp4 [53.8M]
┃ ┗━━43.lesson30-Visdom可視化.mp4 [52.8M]
┣━━9.過擬合 [262.5M]
┃ ┣━━44.lesson31-過擬合與欠擬合.mp4 [42.5M]
┃ ┣━━45.lesson32-Train-Val-Test-交叉驗證-1.mp4 [45.9M]
┃ ┣━━46.lesson32-Train-Val-Test-交叉驗證-2.mp4 [32.3M]
┃ ┣━━47.lesson33-regularization.mp4 [39M]
┃ ┣━━48.lesson34-動量與lr衰減.mp4 [51.5M]
┃ ┗━━49.lesson35-early stopping, dropout, sgd.mp4 [51.2M]
┣━━10.卷積神經網絡CNN [678.5M]
┃ ┣━━50.lesson37-什麼是卷積-1.mp4 [62.8M]
┃ ┣━━51.lesson37-什麼是卷積-2.mp4 [39.6M]
┃ ┣━━52.lesson38-卷積神經網絡-1.mp4 [41.4M]
┃ ┣━━53.lesson38-卷積神經網絡-2.mp4 [62.9M]
┃ ┣━━54.lesson38-卷積神經網絡-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-數據增強Data Argumentation.mp4 [46.8M]
┣━━11.循環神經網絡RNN&LSTM [465M]
┃ ┣━━65.lesson46-時間序列表示.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-時間序列預測.mp4 [53.3M]
┃ ┣━━71.lesson50-RNN訓練難題.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-情感分類實戰.mp4 [68.6M]
┗━━12.對抗生成網絡GAN [316.2M]
┣━━76.lesson54-數據分佈.mp4 [17.4M]
┣━━77.lesson55-畫家的成長曆程.mp4 [28.9M]
┣━━78.lesson56-GAN發展.mp4 [23M]
┣━━79.lesson57-納什均衡-D.mp4 [20.4M]
┣━━80.lesson58-納什均衡-G.mp4 [36.6M]
┣━━81.lesson59-JS散度的弊端.mp4 [36.8M]
┣━━82.lesson60-EM距離.mp4 [17.2M]
┣━━83.lesson61-WGAN與WGAN-GP.mp4 [28.8M]
┣━━84.lesson62-G和D實現.mp4 [17.3M]
┣━━85.lesson63-GAN實戰.mp4 [33.3M]
┣━━86.lesson64-GAN訓練不穩定.mp4 [20.2M]
┗━━87.lesson65-WGAN-GP實戰.mp4 [36.3M]