دانلود کتاب مبانی یادگیری عمیق (یادداشت های سخنرانی)

1 Introduction
1.1 A first model: the Perceptron . . . . . . . . . . . 3
1.2 Optimization . . . . . . . . . . . . . . . . . . . 4
1.3 Wrap-up . . . . . . . . . . . . . . . . . . . . . 8
2 Multi Layer Perceptrons………….. 9
2.1 Stacking layers for better expressivity . . . . . . 9
2.2 Deciding on an MLP architecture . . . . . . . . 11
2.3 Activation functions . . . . . . . . . . . . . . . . 12
2.4 Declaring an MLP in keras . . . . . . . . . . . 13
3 Losses……………….. 17
3.1 Mean Squared Error . . . . . . . . . . . . . . . 17
3.2 Logistic loss . . . . . . . . . . . . . . . . . . . . 18
4 Optimization…………………. 19
4.1 Stochastic Gradient Descent (SGD) . . . . . . . 20
4.2 A note on Adam . . . . . . . . . . . . . . . . . 21
4.3 The curse of depth . . . . . . . . . . . . . . . . 22
4.4 Wrapping things up in keras . . . . . . . . . . 23
4.5 Data preprocessing . . . . . . . . . . . . . . . . 24
5 Regularization………………….. 27
5.1 Early Stopping . . . . . . . . . . . . . . . . . . 27
5.2 Loss penalization . . . . . . . . . . . . . . . . . 29
5.3 DropOut . . . . . . . . . . . . . . . . . . . . . 30
6 Convolutional Neural Networks 33
6.1 ConvNets for time series . . . . . . . . . . . . . 33
6.2 Convolutional neural networks for images . . . . 34
7 Recurrent Neural Networks……………. 41
7.1 “Vanilla” RNNs . . . . . . . . . . . . . . . . . . 42
7.2 Long Short-Term Memory . . . . . . . . . . . . 43
7.3 Gated Recurrent Unit . . . . . . . . . . . . . . . 44
7.4 Conclusion . . . . . . . . . . . . . . . . . . . . 44