Deep_Learning_with_PyTorch_Quick_Start_Guide.pdf

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Deep_Learning_with_PyTorch_Quick_Start_Guide.pdf
Deep Learning with Py Torch Quick Start Guide Copyright o 2018 Packt Publishing All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews Every effort has been made in the preparation of this book to ensure the accuracy of the information presented However, the information contained in this book is sold without warranty, either express or implied. Neither the author(s), nor Packt Publishing or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information Commissioning editor: amey varangaonkar Acquisition Editor: noyonika das Content Development Editor: Kirk dsouza Technical Editor: Sushmeeta Jena Copy Editor: Safis Editing Project Coordinator: hardik bhinde Proofreader: Safis Editing Indexer: Mariamman Chettiyar Graphics: Alishon Mendonsa Production coordinator: nilesh mohite First published December 2018 Production reference: 1201218 Published by Packt Publishing Ltd Livery place 35 Livery street Birmingh B3 2PB, UK ISBN978-1-78953-409-2 www.packtpub.com Mapt mapt.lO Mapt is an online digital library that gives you full access to over 5,000 books and videos, as well as industry leading tools to help you plan your personal development and advance your career. For more information, please visit our website Why subscribe? Spend less time learning and more time coding with practical ebooks and videos from over 4, 000 industry professionals Improve your learning with Skill Plans built especially for you Get a free ebook or video every month e Mapt is fully searchable Copy and paste, print, and bookmark content Packt. com Did you know that Packt offers eBook versions of every book published, with PDF and epubfilesavailableYoucanupgradetotheebookversionatwww.packt.comandasaprint book customer, you are entitled to a discount on the e Book copy. Get in touch with us at customercare@packtpub com for more details Atwww.packt.comyoucanalsoreadacollectionoffreetechnicalarticlessignupfora range of free newsletters and receive exclusive discounts and offers on packt books and eBookS Contributors About the author David julian is a freelance technology consultant and educator. He has worked as a consultant for government, private, and community organizations on a variety of projects, including using machine learning to detect insect outbreaks in controlled agricultura environments Urban Ecological Systems Ltd. Bluesmart Farms), designing and implementing event management data systems Sustainable Industry Expo, Lismore City Council), and designing multimedia interactive installations(Adelaide University). He has also written Designing Machine learning Systems With Python for Packt Publishing and was technical reviewer for Python Machine learning and hands-On Data Structures and algorithms with Python-Second Edition, published by Packt About the reviewer AshishSingh Bhatia has more than 10 years IT experience in different domains, including erP, banking, education and resource management he is a learner, reader and developer at heart. He is passionate about Python, Java, and R. He loves to explore new technologies He has also published two books: Machine learning with Java and r and Natural language Processing with java. apart from this he has also recorded a video tutorial on py torch Packt is searching for authors like you Ifyou'reinterestedinbecominganauthorforPackt,pleasevisitauthorspacktpub.com and apply today we have worked with thousands of developers and tech professionals just like you, to help them share their insight with the global tech community. You can make a general application, apply for a specific hot topic that we are recruiting an author for or submit your own idea Table of contents Preface Chapter 1: Introduction to Py Torch 6 What is torch? 7 Installing PyTorch 9 Digital Ocean Tunneling in to iPython 12 Amazon Web Services(AWs) 13 Basic PyTorch operations 13 Default value initialization 14 Converting between tensors and NumPy arrays 15 Slicing and indexing and reshaping 18 In place operations 20 Loading data 21 Py Torch dataset loaders 23 Displaying an image 25 Dataloader 25 Creating a custom dataset 26 Transforms 28 Image Folder 29 Concatenating datasets 30 Summary 30 Chapter 2: Deep Learning Fundamentals 32 Approaches to machine learning 33 Learning tasks 34 Unsupervised learning 35 Clustering 35 Principle component analysis 35 Reinforcement learning Supervised learning 36 Classification 36 Evaluating classifiers 37 Features 38 Handling text and categories 39 Models 40 Linear algebra review Linear models 44 Gradient descent 46 Multiple features 49 The normal equation 50 Table of contents ogistic regression 50 Nonlinear models 53 Artificial neural networks 54 The perceptron 55 Summary 59 Chapter 3: Computational Graphs and Linear Models 60 autocad 61 Computational graphs 63 Linear models 64 Linear regression in PyTorch 64 Saving models 68 Logistic regression 69 Activation functions in Py torch 71 Multi-class classification example 72 Summary 78 Chapter 4: Convolutional Networks 79 Hyper-parameters and multilayered networks 79 Benchmarking models 81 Convolutional networks 86 A single convolutional layer 86 Multiple kernels 88 Multiple convolutional layers 89 Pooling layers 89 Building a single-layer CNN 90 Building a multiple-layer CNN 92 Batch normalization 94 Summary Chapter 5: other NN Architectures 97 ntroduction to recurrent networks 97 Recurrent artificial neurons 98 mplementing a recurrent network 99 Long short term memory networks 105 Implementing an LSTM 108 Building a language model with a gated recurrent unit 109 Summary 115 Chapter 6: Getting the Most out of PyTorch 116 Multiprocessor and distributed environments 116 Using a GPU 117 Distributed environments 119 torch distributed 119 torch multiprocessing 120 Optimization techniques 121 Optimizer algorithms 121 Table of contents Learning rate scheduler 123 Parameter groups 124 Pretrained models 126 Implementing a pretrained model 128 Summary 133 Other Books You May Enjoy 135 Index 138 Preface PyTorch is surprisingly easy to learn and provides advanced features such as a supporting multiprocessor, as well as distributed and parallel computation py Torch has a library of pre-trained models, providing out-of-the-box solutions for image classification. Py Torcl offers one of the most accessible entry points into cutting-edge deep learning. It is tightly integrated with the Python programming language, so for Python programmers, coding it seems natural and intuitive. The unique, dynamic way of treating computational graphs means that py Torch is both efficient and flexible Who this book is for This book is for anyone who wants a straightforward, practical introduction to dee learning using Py Torch. The aim is to give you an understanding of deep learning models by direct experimentation. This book is perfect for those who are familiar with python know some machine learning basics, and are looking for a way to productively develop their skills. The book will focus on the most important features and give practical examples It assumes you have a working knowledge of Python and are familiar with the relevant mathematical ideas, including with linear algebra and differential calculus. The book provides enough theory to get you up and running without requiring rigorous mathematical understanding By the end of the book, you will have a practical knowledge of deep learning systems and able to apply Py torch models to solve the problems that you care about What this book covers Chapter 1, Introduction to Py Torch, gets you up and running with Py Torch, demonstrates its installation on a variety of platforms, and explores key syntax elements and how to import and use data in PyTorch Chapter 2, Deep learning Fundamentals, is a whirlwind tour of the basics of deep learning, covering the mathematics and theory of optimization linear networks, and neural networks Chapter 3, Computational graphs and Linear models, demonstrates how to calculate the error gradient of a linear network and how to harness it to classify images

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