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首页Python Deep Learning, 1st Edition
Explore and create intelligent systems using cutting-edge deep learning techniques Implement deep learning algorithms and work with revolutionary libraries in Python Get real-world examples and easy-to-follow tutorials on Theano, TensorFlow, H2O and more
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Python Deep Learning
Valentino Zocca
Gianmario Spacagna
Daniel Slater
Peter Roelants
Next generation techniques to revolutionize
computer vision, AI, speech and data analysis
Python Deep Learning
Copyright © 2017 Packt Publishing
First published: April 2017
Production reference: 1270417
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham B3 2PB, UK.
ISBN 978-1-78646-445-3
www.packtpub.com
Preface vii
Chapter 1: Machine Learning – An Introduction 1
What is machine learning? 2
Different machine learning approaches 3
Supervised learning 3
Unsupervised learning 6
Reinforcement learning 8
Steps Involved in machine learning systems 9
Brief description of popular techniques/algorithms 13
Linear regression 14
Decision trees 16
K-means 17
Naïve Bayes 19
Support vector machines 20
The cross-entropy method 22
Neural networks 23
Deep learning 25
Applications in real life 26
A popular open source package 28
Summary 35
Chapter 2: Neural Networks 37
Why neural networks? 38
Fundamentals 39
Neurons and layers 40
Different types of activation function 46
The back-propagation algorithm 51
Linear regression 51
Logistic regression 53
Back-propagation 56
Contents
Applications in industry 60
Signal processing 60
Medical 60
Autonomous car driving 61
Business 61
Pattern recognition 61
Speech production 61
Code example of a neural network for the function xor 62
Summary 68
Chapter 3: Deep Learning Fundamentals 69
What is deep learning? 70
Fundamental concepts 72
Feature learning 73
Deep learning algorithms 83
Deep learning applications 84
Speech recognition 84
Object recognition and classication 86
GPU versus CPU 89
Popular open source libraries – an introduction 91
Theano 91
TensorFlow 92
Keras 92
Sample deep neural net code using Keras 93
Summary 98
Chapter 4: Unsupervised Feature Learning 101
Autoencoders 104
Network design 108
Regularization techniques for autoencoders 111
Denoising autoencoders 111
Contractive autoencoders 112
Sparse autoencoders 114
Summary of autoencoders 116
Restricted Boltzmann machines 117
Hopeld networks and Boltzmann machines 120
Boltzmann machine 123
Restricted Boltzmann machine 126
Implementation in TensorFlow 128
Deep belief networks 133
Summary 134
Chapter 5: Image Recognition 137
Similarities between articial and biological models 138
Intuition and justication 139
Convolutional layers 141
Stride and padding in convolutional layers 148
Pooling layers 150
Dropout 152
Convolutional layers in deep learning 152
Convolutional layers in Theano 154
A convolutional layer example with Keras to recognize digits 156
A convolutional layer example with Keras for cifar10 159
Pre-training 161
Summary 163
Chapter 6: Recurrent Neural Networks and Language Models 165
Recurrent neural networks 166
RNN — how to implement and train 168
Backpropagation through time 169
Vanishing and exploding gradients 172
Long short term memory 175
Language modeling 178
Word-based models 178
N-grams 179
Neural language models 180
Character-based model 185
Preprocessing and reading data 186
LSTM network 187
Training 189
Sampling 191
Example training 192
Speech recognition 193
Speech recognition pipeline 193
Speech as input data 195
Preprocessing 195
Acoustic model 197
Deep belief networks 197
Recurrent neural networks 198
CTC 198
Attention-based models 199
Decoding 199
End-to-end models 200
Summary 201
Bibliography 201
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