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Fundamentals of

Deep

Learning

DESIGNING NEXT-GENERATION

MACHINE INTELLIGENCE ALGORITHMS

Nikhil Buduma

with contributions by Nicholas Locascio

Table of Contents

Preface. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix

1. The Neural Network. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Building Intelligent Machines 1

The Limits of Traditional Computer Programs 2

The Mechanics of Machine Learning 3

The Neuron 7

Expressing Linear Perceptrons as Neurons 8

Feed-Forward Neural Networks 9

Linear Neurons and Their Limitations 12

Sigmoid, Tanh, and ReLU Neurons 13

Softmax Output Layers 15

Looking Forward 15

2. Training Feed-Forward Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

The Fast-Food Problem 17

Gradient Descent 19

The Delta Rule and Learning Rates 21

Gradient Descent with Sigmoidal Neurons 22

The Backpropagation Algorithm 23

Stochastic and Minibatch Gradient Descent 25

Test Sets, Validation Sets, and Overfitting 27

Preventing Overfitting in Deep Neural Networks 34

Summary 37

3. Implementing Neural Networks in TensorFlow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

What Is TensorFlow? 39

How Does TensorFlow Compare to Alternatives? 40

iii

剩余297页未读，继续阅读

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lordcat

2021-01-14

298页，完美书签TXT，2017的书，看目录不错

yinkaisheng-nj

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