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Deep Learning, Vol. 2: From Basics to Practice By 作者: Andrew Glassner Pub Date: 2018 ISBN: n/a Pages: (914 of 1750) Format: PDF Publication Date: February 19, 2018 Language: English ASIN: B079Y1M81K People are using the tools of deep learning to change how we think about science, art, engineering, business, medicine, and even music. This book is for people who want to understand this field well enough to create deep learning systems, train them, and then use them with confidence to make their own contributions. The book takes a friendly, informal approach. Our goal is to make the ideas of this field simple and accessible to everyone, as shown in the Contents below. Since most practitioners today use one of several free, open-source deep-learning libraries to build their systems, the hard part isn’t in the programming. Rather, it’s knowing what tools to use, and when, and how. Building a working deep learning system requires making a series of technically informed choices, and with today’s tools, those choices require understanding what’s going on under the hood. This book is designed to give you that understanding. You’ll be able to choose the right kind of architecture, how to build a system that can learn, how to train it, and then how to use it to accomplish your goals. You’ll be able to read and understand the documentation for whatever library you’d like to use. And you’ll be able to follow exciting, on-going breakthroughs as they appear, because you’ll have the knowledge and vocabulary that let you read new material, and discuss it with other people doing deep learning. The book is extensively illustrated with over 1000 original figures. They are also all available for free download, for your own use. You don’t need any previous experience with machine learning or deep learning for this book. You don’t need to be a mathematician, because there’s nothing in the book harder than the occasional multiplication. You don’t need to choose a particular programming language, or library, or piece of hardware, because our approach is largely independent of those things. Our focus is on the principles and techniques that are applicable to any language, library, and hardware. Even so, practical programming is important. To stay focused, we gather our programming discussions into 3 chapters that show how to use two important and free Python libraries. Both chapters come with extensive Jupyter notebooks that contain all the code. Other chapters also offer notebooks for for every Python-generated figure. Our goal is to give you all the basics you need to understand deep learning, and then show how to use those ideas to construct your own systems. Everything is covered from the ground up, culminating in working systems illustrated with running code. The book is organized into two volumes. Volume 1 covers the basic ideas that support the field, and which form the core understanding for using these methods well. Volume 2 puts these principles into practice. Deep learning is fast becoming part of the intellectual toolkit used by scientists, artists, executives, doctors, musicians, and anyone else who wants to discover the information hiding in their data, paintings, business reports, test results, musical scores, and more. This friendly, informal book puts those tools into your pocket.

This is one of my most favorite books that teache deep learning from the very basic to advance level. The book does not emphasize on mathematics but explains the logic behind each deep learning algorithm in plain English. Even if you are the new guy in deep learning or you are already at an advance level, this book is for you. Vol 2 will be coming shortly

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In September 2013, while browsing Hacker News, I stumbled across an article in Wired about a new technology called “Docker.” 1 As I read it, I became increasingly excited as I realized Docker’s revolutionary potential. The company I’d worked at for over a decade was struggling to deliver software quickly enough. Provisioning environments was a costly, time-consuming, manual, and inelegant affair. Continuous integration was barely existent, and setting up devel- opment environments was an exercise in patience. As my job title included the words “DevOps Manager,” I was peculiarly motivated to solve these problems! I recruited a couple of motivated coworkers (one of them now my coauthor) via a company mailing list, and together our skunkworks team labored to turn a beta tool into a business advantage, reducing the high costs of VM s and enabling new ways of thinking about building and deploying software. We even built and open sourced an automation tool (ShutIt) to suit our organization’s delivery needs. Docker gave us a packaged and maintained tool that solved many problems that would have been effectively insuperable had we taken it upon ourselves to solve them. This was open source at its best, empowering us to take on a challenge using our spare time, overcoming technical debt, and learning lessons daily. Lessons not only about Docker, but about continuous integration, continuous delivery, packaging, automa- tion, and how people respond to speedy and disruptive technological change. 1 http://www.wired.com/2013/09/docker/ PREFACE xviii For us, Docker is a remarkably broad tool. Wherever you run software using Linux, Docker can impact it. This makes writing a book on the subject challenging, because the landscape is as broad as software itself. The task is made more onerous by the extraordinary rate at which the Docker ecosystem is producing solutions to meet the needs that emerge from such a fundamental change in software production. Over time, the shape of problems and solutions became familiar to us, and in this book we’ve endeavored to pass on this experience. This will enable you to figure out solu- tions to your particular technical and business constraints. When giving talks at meetups, we’re struck by how quickly Docker has become effective within organizations willing to embrace it. This book mirrors how we used Docker, going from our desktops, through the DevOps pipeline, and all the way to production. As a consequence, this book is sometimes unorthodox, but as engineers we believe that purity must sometimes give way to practicality, especially when it comes to saving money! Everything in this book is based on real lessons from the field, and we hope you benefit from our hard-won experience.

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