1.0 How Regularization Works | End to End Machine Learning

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313. Advanced Neural Network Methods

Introduction

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A Roadmap
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1 Regularization

1.0 How Regularization Works
1.1 Constructing a Network with Regularization (7:14)
1.2 Executing Regularization during Stochastic Gradient Descent (3:35)
1.3 L1 Regularization (LASSO) (4:45)
1.4 L2 Regularization (Ridge or Tikhonov) (2:15)
1.5 Regularization in Action (2:55)
1..6 Custom Regularizers (3:25)

2 Dropout

2.0 How Dropout Works (6:26)
2.1 Set dropout rates during layer creation (2:09)
2.2 Drop out nodes during training (4:48)
2.3 Dropout in Action (1:56)

3 Custom layers and Skip-layers

3.0 What is a Computation Graph and Who Cares? (9:10)
3.1 Computation Graph for Custom Layers (3:48)
3.2 Build a Model with Custom Layers (3:05)
3.3 Run a Model with a Branching Computation Graph (4:28)
3.4 Build a Generic Layer (3:04)
3.5 Update the Dense Layer (3:16)
3.6 Build a Normalization Layer (4:02)
3.7 Build a Difference-Calculating Layer (3:40)
3.8 Test Autoencoder Performance (4:04)

4 Optimizers

4.0 Separate the Optimizer from the Regularizers (5:43)
4.1 Separate the Regularizers into Pre- and Post-Optimizer (2:56)
4.2 Stochastic Gradient Descent (SGD) (6:30)
4.3 Momentum (4:53)
4.4 Adam (5:03)
4.5 Experimental Optimizers (2:24)
4.6 Optimizer Comparison (3:53)

5 Initialization

5.0 Glorot Initialization (5:52)
5.1 He Initialization (1:52)
5.2 Custom Initializer (2:16)
5.3 Initializer Comparison (3:32)

6 Martian data

6.0 Reporting (9:07)
6.1 Enable one-line download and install (6:31)
6.2 Get images of Mars (3:33)
6.3 Build Martian images autoencoder example (8:35)
6.4 Run autoencoder on Martian images (5:53)

1.0 How Regularization Works

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