Deep Learning with Python: A Hands-on Introduction

Nikhil Ketkar, "Deep Learning with Python: A Hands-on Introduction" English | ISBN: 1484227654 | 2017 | 143 pages | PDF | 7 MB Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms. This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included. Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to production Who This Book Is For Software developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
Deep learning with Python: A Hands-on Introduction Nikhil Ketkar Bangalore, Karnataka, India ISBN-13(pbk):978-1-4842-2765-7 ISBN-13( electronic):978-1-4842-2766-4 DOI10.1007/978-1-4842-2766-4 Library of Congress Control Number: 2017939734 Copyright o 2017 by Nikhil Ketkar This work is subject to copyright All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse ofillustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed Trademarked names, logos, and images may appear in this book. 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Cross entropy 23 Squared error Summary of Loss Functions…… 25 Types of Units/Activation Functions/ Layers 25 Linear unit 26 Sigmoid Unit 26 Softmax layer Rectified Linear Unit (relu) 27 Hyperbolic Tangent Neural network hands-on with Autograd 31 Summary 31 Chapter 4: Introduction to Theano ■■■■■ 33 What is theano 33 Theano hands-On …34 Summary 59 Chapter 5: Convolutional Neural Networks an 61 Convolution Operation ,61 Pooling Operation 68 Convolution-Detector-Pooling building block 70 Convolution variants 74 Intuition behind cnns 75 Summary 76 Chapter 6: Recurrent Neural Networks rnn Basics Training rNNs..……82 Bidirectional rnns 89 CONTENTS Gradient Explosion and Vanishing 90 Gradient Clipping nnnnnnnnnnnnnnnnnnnnnnnnnnnD 91 Long short Term Memory 93 Summary 94 Chapter 7: Introduction to Keras 5 Summary.......…109 Chapter 8: Stochastic Gradient Descent ■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■■ 111 Optimization problems Method of Steepest Descent nnnnnnnnnnnnnnnnnnnnnn 112 Batch, Stochastic( Single and Mini- batch) Descent....,………113 Batch …114 Stochastic Single Example....................... 114 Stochastic Mini-batch 114 Batch vs stochastic mmmmmmmmm. 114 Challenges with SGD Local minima 114 Saddle points 115 Selecting the Learning Rate 116 Slow Progress in Narrow valleys ……117 Algorithmic variations on SGD …17 Momentum 118 Nesterov Accelerated gradient(NAS) 119 Annealing and learning rate schedules 119 Adagrad...,.,.,.,.,, 119 RMSPr Adadelta∴ 121 Adam 121 Resilient Backpropagation 面日面重日面日面面日面日日面日面日面日日面面日面日日日面日面日面日面面面日日面面日面重日日日面日面日面日面面日面日日面日面日面日日面面日面日 121 Equilibrated SGD 122 CONteNtS Tricks and tips for using sGD ,122 Preprocessing Input Data 122 Choice of activation function…… 122 Preprocessing Target value.............. 123 Initializing parameters..e.ne 123 Shuffling data 123 Batch normalization 123 Early Stopping… Gradient noise 123 Parallel and distributed sgdagmmmggmg. 124 Hogwild .124 Downpour 124 Hands-on sgd with downhill 125 Summary 130 Chapter 9: Automatic Differentiation an n131 Numerical Differentiation 131 Symbolic Differentiation. Automatic Differentiation fundamentals 133 Forward/Tangent Linear Mode …134 Reverse/Cotangent/Adjoint Linear Mode..............................138 Implementation of Automatic differentiation 141 Hands-on Automatic Differentiation with autograd 143 Summary… 146 Chapter 10: Introduction to GPUs BRIBIERBBRBRBRRBRRRRRIRRERERIRRRIRII 147 Summary… 156 Index uu 157 About the author Nikhil Ketkar currently leads the Machine Learning Platform team at Flipkart, Indias largest e-commerce company He received his PhD from Washington State University. Following that, he conducted postdoctoral research at University of North Carolina at Charotte, which was followed A y a brief stint in high frequency trading at TransMarket in Chicago. More tly, he led the data mining team in Guavus, a startup doing big data analytics in the telecom domain and Indix, a startup doing data science the e-commerce domain. His research interests include machine learning na gra