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首页Fundamentals of Deep Learning.pdf
This booked is aimed an audience with a basic operating understanding of calculus, matrices, and Python programming. Approaching this material without this background is possible, but likely to be more challenging. Background in linear algebra may also be helpful in navigating certain sections of mathematical exposition. By the end of the book, we hope that our readers will be left with an intuition for how to approach problems using deep learning, the historical context for modern deep learning approaches, and a familiarity with implementing deep learning algorithms using the TensorFlow open source library.
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978-1-491-92561-4
[TI]
Fundamentals of Deep Learning
by Nikhil Buduma and Nicholas Lacascio
Copyright © 2017 Nikhil Buduma. All rights reserved.
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June 2017:
First Edition
Revision History for the First Edition
2017-05-25: First Release
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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
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