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Essential Excerpts Essential Excerpts Eli Stevens Luca Antiga MANNING Deep Learning with PyTorch Essential Excerpts Eli Stevens and Luca Antiga Manning Author Picks Copyright 2019 Manning Publications To pre-order or learn more about this book go to www.manning.com are printed on paper that is at least 15 percent recycled and processed without the use of elemental chlorine. Manning Publications Co. 20 Baldwin Road Technical PO Box 761 Shelter Island, NY 11964 Cover designer: Leslie Haimes ISBN: 9781617297120 Printed in the United States of America 1 2 3 4 5 6 7 8 9 10 - EBM - 24 23 22 21 20 19 contents preface v 1Introducing deep learning and the PyTorch library 1 1.1 What is PyTorch? 2 1.2 What is this book? 2 1.3 Why PyTorch? 3 1.4 PyTorch has the batteries included 10 2It starts with a tensor 15 2.1 Tensor fundamentals 18 2.2 Tensors and storages 22 2.3 Size, storage offset, and strides 24 2.4 Numeric types 30 2.5 Indexing tensors 31 2.6 NumPy interoperability 31 2.7 Serializing tensors 32 2.8 Moving tensors to the GPU 34 2.9 The tensor API 35 3Real-world data representation with tensors 39 3.1 Tabular data 40 3.2 Time series 49 3.3 Text 54 3.4 Images 60 3.5 Volumetric data 63 iii iv CONTENTS 4The mechanics of learning 67 4.1 Learning is parameter estimation 70 4.2 PyTorch’s autograd: Backpropagate all things 83 5Using a neural network to fit your data 101 5.1 Artificial neurons 102 5.2 The PyTorch nn module 110 5.3 Subclassing nn.Module 120 index 127 about the authors Eli Stevens has worked in Silicon Valley for the past 15 years as a software engineer, and the past 7 years as Chief Technical Officer of a startup making medical device software. Luca Antiga is co-founder and CEO of an AI engineering company located in Bergamo, Italy, and a regular contributor to PyTorch. vi Save 50% on the full book – eBook, pBook, and MEAP. Enter ebstevens50 in the Promotional Code box when you checkout. Only at manning.com. Deep Learning with PyTorch by Eli Stevens and Luca Antiga ISBN 9781617295263 400 pages (estimated) $49.99 Publication in Winter, 2019 (estimated) Introducing deep learning and the PyTorch library This chapter covers What this book will teach you PyTorch’s role as a library for building deep learning projects The strengths and weaknesses of PyTorch The hardware you’ll need to follow along with the examples We’re living through exciting times. The landscape of what computers can do is changing by the week. Tasks that only a few years ago were thought to require higher cognition are getting solved by machines at near-superhuman levels of per- formance. Tasks such as describing a photographic image with a sentence in idiom- atic English, playing complex strategy game, and diagnosing a tumor from a radiological scan are all approachable now by a computer. Even more impressively, computers acquire the ability to solve such tasks through examples, rather than human-encoded of handcrafted rules. It would be disingenuous to assert that machines are learning to “think” in any human sense of the word. Rather, a general class of algorithms is able to approxi- 1 2 CHAPTER 1 Introducing deep learning and the PyTorch library mate complicated, nonlinear processes extremely effectively. In a way, we’re learning that intelligence, as we subjectively perceive it, is a notion that’s often conflated with self-awareness, and self-awareness definitely isn’t required to solve or carry out these kinds of problems. In the end, the question of computer intelligence may not even be important. As pioneering computer scientist Edsger W. Dijkstra said in “The Threats to Computing Science,” Alan M. Turing thought about . the question of whether Machines Can Think, a question . about as relevant as the question of whether Submarines Can Swim. That general class of algorithms we’re talking about falls under the category of deep learning, which deals with training mathematical entities named deep neural networks on the basis of examples. Deep learning leverages large amounts of data to approximate complex functions whose inputs and outputs are far apart, such as an image (input) and a line of text describing the input (output); a written script (input) and a natural- sounding voice reciting the script (output); or, even more simply, associating an image of a golden retriever with a flag that indicates that a golden retriever is present. This capability allows developers to create programs with functionality that until recently was the exclusive domain of human beings. 1.1 What is PyTorch? PyTorch is a library for Python programs that facilitates building deep learning proj- ects. It emphasizes flexibility and allows deep learning models to be expressed in idi- omatic Python. This approachability and ease of use found early adopters in the research community, and in the years since the library’s release, it has grown into one of the most prominent deep learning tools for a broad range of applications. PyTorch provides a core data structure, the Tensor, a multidimensional array that has many similarities with NumPy arrays. From that foundation, a laundry list of fea- tures was built to make it easy to get a project up and running, or to design and train investigation into a new neural network architecture. Tensors accelerate mathematical operations (assuming that the appropriate combination of hardware and software is present), and PyTorch has packages for distributed training, worker processes for effi- cient data loading, and an extensive library of common deep learning functions. As Python is for programming, PyTorch is both an excellent introduction to deep learning and a tool usable in professional contexts for real-world, high-level work. We believe that PyTorch should be the first deep learning library you learn. Whether it should be the last is a decision that we’ll leave to you. 1.2 What is this book? This book is intended to be a starting point for software engineers, data scientists, and motivated students who are fluent in Python and want to become comfortable using PyTorch to build deep learning projects. To that end, we take a hands-on approach; we encourage you to keep your computer at the ready so that you can play with the examples and take them a step further. Why PyTorch? 3 Though we stress the practical applications, we also believe that providing an accessible introduction to foundational deep learning tools like PyTorch is more than a way to facilitate the acquisition of new technical skills. It is also a step toward equip- ping a new generation of scientists, engineers, and practitioners from a wide range of disciplines with a working knowledge of the tools that will be the backbone of many software projects during the decades to come. To get the most out of this book, you need two things: Some experience programming in Python—We’re not going to pull any punches on that one: you’ll need to be up on Python data types, classes, floating-point num- bers, and the like. Willingness to dive in and get your hands dirty—It’ll be much easier for you to learn if you follow along with us. Deep learning is a huge space. In this book, we’ll be covering a tiny part of that space—specifically, using PyTorch for smaller-scope projects. Most of the motivating examples use image processing of 2D and 3D data sets. We focus on practical PyTorch, with the aim of covering enough ground to allow you to solve realistic problems with deep learning or explore new models as they pop up in research literature. A great resource for the latest publications related to deep learning research is the ArXiV public preprint repository, hosted at https://arxiv.org.1 1.3 Why PyTorch? As we’ve said, deep learning allows you to carry out a wide range of complicated tasks—such as performing machine translation, playing strategy games, and identify- ing objects in cluttered scenes—by exposing your model to illustrative examples. To do so in practice, you need tools that are flexible so that they can be adapted to your specific problem and efficient, to allow training to occur over large amounts of data in reasonable times. You also need the trained network to perform correctly in the pres- ence of uncertainty in the inputs. In this section, we take a look at some of the reasons why we decided to use PyTorch. PyTorch is easy to recommend because of its simplicity. Many researchers and prac- titioners find it easy to learn, use, extend, and debug. It’s Pythonic, and although (like any complicated domain) it has caveats and best practices, using the library generally feels familiar to developers who have used Python previously. For users who are familiar with NumPy arrays, the PyTorch Tensor class will be immediately familiar. PyTorch feels like NumPy, but with GPU acceleration and auto- matic computation of gradients, which makes it suitable for calculating backward pass data automatically starting from a forward expression. The Tensor API is such that the additional features of the class relevant to deep learning are unobtrusive; the user is mostly free to pretend that those features don’t exist until need for them arises. 1 We also recommed http://www.arxiv-sanity.com to help organize research papers of interest. 4 CHAPTER 1 Introducing deep learning and the PyTorch library A design driver for PyTorch is expressivity, allowing a developer to implement com- plicated models without undue complexity being imposed by the library.
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