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Ebook Deep Learning A Practitioner’s Approach By Josh Patterson And Adam Gibson (2017) | Epub, Azw3

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    Deep Learning
    A Practitioner’s Approach
    by Josh Patterson and Adam Gibson
    OReilly | 2017 | Database Storage & Design, Data Modeling & Design | Epub, Azw3 | 22.6 Mb
    [​IMG]

    Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks.

    Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J.
    • Dive into machine learning concepts in general, as well as deep learning in particular
    • Understand how deep networks evolved from neural network fundamentals
    • Explore the major deep network architectures, including Convolutional and Recurrent
    • Learn how to map specific deep networks to the right problem
    • Walk through the fundamentals of tuning general neural networks and specific deep network architectures
    • Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool
    • Learn how to use DL4J natively on Spark and Hadoop
    Get This Book From Amazon Online Store >>

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    What’s in This Book?

    The first four chapters of this book are focused on enough theory and fundamentals to give you, the practitioner, a working foundation for the rest of the book. The last five chapters then work from these concepts to lead you through a series of practical paths in deep learning using DL4J:
    • Building deep networks
    • Advanced tuning techniques
    • Vectorization for different data types
    • Running deep learning workflows on Spark
    Code:
    DL4J AS SHORTHAND FOR DEEPLEARNING4J
    We use the names DL4J and Deeplearning4j interchangeably in this book. Both terms refer to the suite of tools in the Deeplearning4j library.
    We designed the book in this manner because we felt there was a need for a book covering “enough theory” while being practical enough to build production-class deep learning workflows. We feel that this hybrid approach to the book’s coverage fits this space well.
    Chapter 1 is a review of machine learning concepts in general as well as deep learning in particular, to bring any reader up to speed on the basics needed to understand the rest of the book. We added this chapter because many beginners can use a refresher or primer on these concepts and we wanted to make the project accessible to the largest audience possible.
    Chapter 2 builds on the concepts from Chapter 1 and gives you the foundations of neural networks. It is largely a chapter in neural network theory but we aim to present the information in an accessible way. Chapter 3 further builds on the first two chapters by bringing you up to speed on how deep networks evolved from the fundamentals of neural networks.
    Chapter 4 then introduces the four major architectures of deep networks and provides you with the foundation for the rest of the book.
    In Chapter 5, we take you through a number of Java code examples using the techniques from the first half of the book. Chapters 6 and 7 examine the fundamentals of tuning general neural networks and then how to tune specific architectures of deep networks. These chapters are platform-agnostic and will be applicable to the practitioner of any deep learning library. Chapter 8 is a review of the techniques of vectorization and the basics on how to use DataVec (DL4J’s ETL and vectorization workflow tool). Chapter 9 concludes the main body of the book with a review on how to use DL4J natively on Spark and Hadoop and illustrates three real examples that you can run on your own Spark clusters.
    The book has many Appendix chapters for topics that were relevant yet didn’t fit directly in the main chapters. Topics include:
    • Artificial Intelligence
    • Using Maven with DL4J projects
    • Working with GPUs
    • Using the ND4J API
    • and more
    Who Is “The Practitioner”?
    Today, the term “data science” has no clean definition and often is used in many different ways. The world of data science and artificial intelligence (AI) is as broad and hazy as any terms in computer science today. This is largely because the world of machine learning has become entangled in nearly all disciplines.

    This widespread entanglement has historical parallels to when the World Wide Web (90s) wove HTML into every discipline and brought many new people into the land of technology. In the same way, all types—engineers, statisticians, analysts, artists—are entering the machine learning fray every day. With this book, our goal is to democratize deep learning (and machine learning) and bring it to the broadest audience possible.
    If you find the topic interesting and are reading this preface—you are the practitioner, and this book is for you.

    Who Should Read This Book?
    As opposed to starting out with toy examples and building around those, we chose to start the book with a series of fundamentals to take you on a full journey through deep learning.

    We feel that too many books leave out core topics that the enterprise practitioner often needs for a quick review. Based on our machine learning experiences in the field, we decided to lead-off with the materials that entry-level practitioners often need to brush up on to better support their deep learning projects.

    You might want to skip Chapters 1 and 2 and get right to the deep learning fundamentals. However, we expect that you will appreciate having the material up front so that you can have a smooth glide path into the more difficult topics in deep learning that build on these principles. In the following sections, we suggest some reading strategies for different backgrounds.

    The Enterprise Machine Learning Practitioner
    We split this category into two subgroups:
    • Practicing data scientist
    • Java engineer
    The practicing data scientist
    This group typically builds models already and is fluent in the realm of data science. If this is you, you can probably skip Chapter 1 and you’ll want to lightly skim Chapter 2. We suggest moving on to Chapter 3 because you’ll probably be ready to jump into the fundamentals of deep networks.

    The Java engineer

    Java engineers are typically tasked with integrating machine learning code with production systems. If this is you, starting with Chapter 1 will be interesting for you because it will give you a better understanding of the vernacular of data science. Appendix E should also be of keen interest to you because integration code for model scoring will typically touch ND4J’s API directly.

    The Enterprise Executive
    Some of our reviewers were executives of large Fortune 500 companies and appreciated the content from the perspective of getting a better grasp on what is happening in deep learning. One executive commented that it had “been a minute” since college, and Chapter 1 was a nice review of concepts. If you’re an executive, we suggest that you begin with a quick skim of Chapter 1 to reacclimate yourself to some terminology. You might want to skip the chapters that are heavy on APIs and examples, however.

    The Academic
    If you’re an academic, you likely will want to skip Chapters 1 and 2 because graduate school will have already covered these topics. The chapters on tuning neural networks in general and then architecture-specific tuning will be of keen interest to you because this information is based on research and transcends any specific deep learning implementation. The coverage of ND4J will also be of interest to you if you prefer to do high-performance linear algebra on the Java Virtual Machine (JVM).

    Conventions Used in This Book
    The following typographical conventions are used in this book:
    Italic
    Indicates new terms, URLs, email addresses, filenames, and file extensions.​
    Constant width
    Used for program listings, as well as within paragraphs to refer to program elements such as variable or function names, databases, data types, environment variables, statements, and keywords. Also used for module and package names, and to show commands or other text that should be typed literally by the user and the output of commands.​
    Constant width italic
    Shows text that should be replaced with user-supplied values or by values determined by context.​
    Code:
    TIP
    This element signifies a tip or suggestion.
    Code:
    NOTE
    This element signifies a general note.
    Code:
    WARNING
    This element signifies a warning or caution.
    Using Code Examples
    Supplemental material (virtual machine, data, scripts, and custom command-line tools, etc.) is available for download at
    Code:
    https://github.com/deeplearning4j/oreilly-book-dl4j-examples
    This book is here to help you get your job done. In general, if example code is offered with this book, you may use it in your programs and documentation. You do not need to contact us for permission unless you’re reproducing a significant portion of the code. For example, writing a program that uses several chunks of code from this book does not require permission. Selling or distributing a CD-ROM of examples from O’Reilly books does require permission. Answering a question by citing this book and quoting example code does not require permission. Incorporating a significant amount of example code from this book into your product’s documentation does require permission.

    We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: “Deep Learning: A Practitioner’s Approach by Josh Patterson and Adam Gibson (O’Reilly). Copyright 2017 Josh Patterson and Adam Gibson, 978-1-4919-1425-0.”

    If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at .Administrative Notes
    In Java code examples, we often omit the import statements. You can see the full import listings in the actual code repository. The API information for DL4J, ND4J, DataVec, and more is available on this website:
    Code:
    http://deeplearning4j.org/documentation
    You can find all code examples at:
    Code:
    https://github.com/deeplearning4j/oreilly-book-dl4j-examples
    For more resources on the DL4J family of tools, check out this website:
    Code:
    http://deeplearning4j.org
    
    O’Reilly Safari
    Safari (formerly Safari Books Online) is a membership-based training and reference platform for enterprise, government, educators, and individuals.
    Members have access to thousands of books, training videos, Learning Paths, interactive tutorials, and curated playlists from over 250 publishers, including O’Reilly Media, Harvard Business Review, Prentice Hall Professional, Addison-Wesley Professional, Microsoft Press, Sams, Que, Peachpit Press, Adobe, Focal Press, Cisco Press, John Wiley & Sons, Syngress, Morgan Kaufmann, IBM Redbooks, Packt, Adobe Press, FT Press, Apress, Manning, New Riders, McGraw-Hill, Jones & Bartlett, and Course Technology, among others.
    For more information, please visit
    Code:
    http://oreilly.com/safari.
    About the Authors
    Josh Patterson currently is the head of field engineering at Skymind. Josh previously ran a consultancy in the big data/machine learning/deep learning space. Previously, he worked as a principal solutions architect at Cloudera and as a machine learning/distributed systems engineer at the Tennessee Valley Authority, where he brought Hadoop into the smart grid with the openPDC project. Josh has a Master’s in computer science from the University of Tennessee at Chattanooga where he published research on mesh networks (tinyOS) and social insect optimization algorithms. Josh has more than 17 years in software development and is very active in the open source space, contributing to projects such as DL4J, Apache Mahout, Metronome, IterativeReduce, openPDC, and JMotif.

    Adam Gibson is a deep learning specialist based in San Francisco. He works with Fortune 500 companies, hedge funds, PR firms, and startup accelerators to create their machine learning projects. Adam has a strong track record helping companies handle and interpret big realtime data. He has been a computer nerd since the age of 13, and actively contributes to the open source community through
    Code:
    http://deeplearning4j.org.
    Download:
    Deep Learning A Practitioner’s Approach (2017) | Epub, Azw3 | 26.6 MB |

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    কঠিন বই। মাথার উপ্রে দিয়া গেল!!
     
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    morsalin Junior Member Member

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    আমিও কিছুই বুঝলাম না। এই বই কারা পড়ে মামা?
     
  4. kazi Ifaz
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    kazi Ifaz Senior Member Member

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    great share. thnx.
     

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