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Deep Learning

Overview of attention for book
Deep Learning
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 The Deep Learning Revolution
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    Chapter 2 Probabilities
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    Chapter 3 Standard Distributions
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    Chapter 4 Single-layer Networks: Regression
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    Chapter 5 Single-layer Networks: Classification
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    Chapter 6 Deep Neural Networks
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    Chapter 7 Gradient Descent
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    Chapter 8 Backpropagation
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    Chapter 9 Regularization
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    Chapter 10 Convolutional Networks
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    Chapter 11 Structured Distributions
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    Chapter 12 Transformers
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    Chapter 13 Graph Neural Networks
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    Chapter 14 Sampling
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    Chapter 15 Discrete Latent Variables
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    Chapter 16 Continuous Latent Variables
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    Chapter 17 Generative Adversarial Networks
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    Chapter 18 Normalizing Flows
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    Chapter 19 Autoencoders
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    Chapter 20 Diffusion Models
Attention for Chapter 4: Single-layer Networks: Regression
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