Deep Learning with Python[www.rejoiceblog.com].pdf

文件名称: Deep Learning with Python[www.rejoiceblog.com].pdf

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详细说明：非常棒的入门书籍，详细介绍了深度学习的基本概念、适用范围以及在Python下的实现方法。 Deep learning FRANCOIS CHOLLET MANNING SHELTER ISLAND For online information and ordering of this and other manning books, please visit www.manning.com.Thepublisheroffersdiscountsonthisbookwhenorderedinquantity For more information, please contact Special Sales Department Manning publications co 20 Baldwin road POBoⅹ761 Shelter island. ny 11964 Emailordersmanning.com 2018 by Manning Publications Co. All rights reserved No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by means electronic, mechanical, photocopying, or otherwise, without prior written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. Where those designations appear in the book, and Manning Publications was aware of a trademark claim, the designations have been printed in initial caps or all caps o Recognizing the importance of preserving what has been written, it is Mannings policy to have the books we publish printed on acid-free paper, and we exert our best efforts to that end Recognizing also our responsibility to conserve the resources of our planet, manning books are printed on paper that is at least 15 percent recycled and processed without the use of elemental chlorine Manning Publications Co Development editor: Toni arritola 20 Baldwin road Technical development editor: Jerry gaines PO Box 761 Review editor: Aleksandar dragosavljevic Shelter island. ny1l964 Project editor: Tiffany Taylor Copyeditor: Tiffany Taylor Proofreader: Katie Tennant Technical proofreaders: Alex Ott and richard tobias Typesetter: Dottie marsico Cover designer: Marija Tudor ISBN9781617294433 Printed in the United states of america 12345678910-EBM-222120191817 brief contents PART 1 FUNDAMENTALS OF DEEP LEARNING 1 What is deep learning? 3 Before we begin: the mathematical building blocks of neural networks 25 3 Getting started with neural networks 56 4 Fundamentals of machine learning 98 PART 2 DEEP LEARNING IN PRACTICE................117 Deep learning for computer vision 119 6 Deep learning for text and sequences 178 7 Advanced deep-learning best practices 238 Generative deep learning 269 9■ Conclusions314 contents reface x222 acknowledgments xu bout this book xvi about the author xx about the cover xxi PART 1 FUNDAMENTALS OF DEEP LEARNING What is deeb learning? 3 1.1 Artificial intelligence, machine learning, and deep learning 4 Artificial intelligence 4 Machine leaning 4. Learning representations from data6·The“dep” in deep learning8 Understanding how deep learning works, in three figures g What deep learning has achieved so far 11. Don't believe the short-term hype 12. The promise of A 13 1.2 Before deep learning: a brief history of machine earning 1 4 Probabilistic modeling 14. Early neural networks 14 Kernel methods 15" Decision trees, random forests and gradient boosting machines 16. Back to neural networks 17. What makes deep learning different 17 The modern machine-learning landscape 18 CONTENTS 1. 3 Why deep learning? Why now? 20 Hardware20·Data21· Algorithms21.Ame wave of investment 22 The democratization of deep learnin g 23· Will it last?23 2 Before we begin: the mathematical building blocks of meural networks 25 2.1 A first look at a neural network 27 2.2 Data representations for neural networks 31 Scalars(od tensors) 31. vectors(lD tensors) 31 Matrices(2D tensors) 31.D tensors and higher- dimensional tensors 32. Key attributes 32 Manipulating tensors in Numpy 34. The notion of data batches 34 Real-world examples of data tensors 35- Vector data 35. Timeseries data or sequence data 35. Image data 36. video data 37 2.3 The gears of neural networks: tensor operations 38 Element-wise operations 38. Broadcasting 39. Tensor dot 40- Tensor reshaping 42. Geometric interpretation of tensor operations 43. A geometric interpretation of deep learning 44 2.4 The engine of neural networks: gradient-based optimization 46 What's a derivative? 47. Derivative of a tensor operation he gradient≠8· Stochastic gradient descent48 Chaining derivatives: the Backpropagation algorithm 51 2.5 Looking back at our first example 58 2.6 Chapter summary 55 Getting started with neural networks 56 3.1 Anatomy of a neural network 58 Layers: the building blocks of deep learning 58. Models networks of layers 59: Loss functions and optimizers: keys to configuring the learning process 60 3.2 Introduction to Keras 61 Keras, TensorFlow, Theano, and CNTK 62. Developing with Keras: a quick overview 62 3.3 Setting up a deep-learning workstation 65 Jupyter notebooks: the preferred way to run deep -learning experiments 65. Getting Keras running: two options 66 CONTENTS Running deep-learning jobs in the cloud: pros and cons 66 What is the best GPU for deep leaning? 66 8.4 Classifying movie reviews: a binary classification example 68 The IMdb dataset 68 Preparing the data 69 Building your network 70. Validating your approach 73 Using a trained network to generate predictions on new data 76. Further experiments 77. Wrapping up 77 3.5 Classifying newswires: a multiclass classification example 78 The reuters dataset 78 Preparing the data 79 Building your network 79. Validating your approach 80 Generating predictions on new data 83. A different way to handle the labels and the loss 83. The importance of having sufficiently large intermediate layers 83. Further experiments 84. Wrapping up 84 3.6 Predicting house prices: a regression example 85 The Boston Housing Price dataset 85. Preparing the data86· Building your network86· validating your approach using K fold validation 87. Wrapping up 91 3.7 Chapter summary 92 Fundamentals of machine learning 93 4.1 Four branches of machine learning 94 Supervised learning 94. Unsupervised learning 94 Self-supervised leaning 94. Reinforcement learning 95 4.2 Evaluating machine-learning models 97 Training, validation, and test sets 97. Things to keep in mind 100 4.3 Data preprocessing, feature engineering, and feature learning 101 Data preprocessing for neural networks 101. Feature engineering 102 4.4 Overfitting and underfitting 104 Reducing the network's size 104. Adding weight regularization 107. Adding dropout 109 4.5 The universal workflow of machine learning lll Defining the problem and assembling a dataset 111 Choosing a measure of success 112. Deciding on an

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