• Complain

Bosagh Zadeh Reza - Tensorflow for deep learning from linear regression to reinforcement learning

Here you can read online Bosagh Zadeh Reza - Tensorflow for deep learning from linear regression to reinforcement learning full text of the book (entire story) in english for free. Download pdf and epub, get meaning, cover and reviews about this ebook. City: Beijing;Boston;Farnham;Sebastopol;Tokyo, year: 2018, publisher: OReilly Media, genre: Computer. Description of the work, (preface) as well as reviews are available. Best literature library LitArk.com created for fans of good reading and offers a wide selection of genres:

Romance novel Science fiction Adventure Detective Science History Home and family Prose Art Politics Computer Non-fiction Religion Business Children Humor

Choose a favorite category and find really read worthwhile books. Enjoy immersion in the world of imagination, feel the emotions of the characters or learn something new for yourself, make an fascinating discovery.

Bosagh Zadeh Reza Tensorflow for deep learning from linear regression to reinforcement learning
  • Book:
    Tensorflow for deep learning from linear regression to reinforcement learning
  • Author:
  • Publisher:
    OReilly Media
  • Genre:
  • Year:
    2018
  • City:
    Beijing;Boston;Farnham;Sebastopol;Tokyo
  • Rating:
    5 / 5
  • Favourites:
    Add to favourites
  • Your mark:
    • 100
    • 1
    • 2
    • 3
    • 4
    • 5

Tensorflow for deep learning from linear regression to reinforcement learning: summary, description and annotation

We offer to read an annotation, description, summary or preface (depends on what the author of the book "Tensorflow for deep learning from linear regression to reinforcement learning" wrote himself). If you haven't found the necessary information about the book — write in the comments, we will try to find it.

Learn how to solve challenging machine learning problems with Tensorflow, Googles revolutionary new system for deep learning. If you have some background with basic linear algebra and calculus, this practical book shows you how to buildand when to usedeep learning architectures. Youll learn how to design systems capable of detecting objects in images, understanding human speech, analyzing video, and predicting the properties of potential medicines. TensorFlow for Deep Learning teaches concepts through practical examples and builds understanding of deep learning foundations from the ground up.

Bosagh Zadeh Reza: author's other books


Who wrote Tensorflow for deep learning from linear regression to reinforcement learning? Find out the surname, the name of the author of the book and a list of all author's works by series.

Tensorflow for deep learning from linear regression to reinforcement learning — read online for free the complete book (whole text) full work

Below is the text of the book, divided by pages. System saving the place of the last page read, allows you to conveniently read the book "Tensorflow for deep learning from linear regression to reinforcement learning" online for free, without having to search again every time where you left off. Put a bookmark, and you can go to the page where you finished reading at any time.

Light

Font size:

Reset

Interval:

Bookmark:

Make
TensorFlow for Deep Learning

by Bharath Ramsundar and Reza Bosagh Zadeh

Copyright 2018 Reza Zadeh, Bharath Ramsundar. All rights reserved.

Printed in the United States of America.

Published by OReilly Media, Inc. , 1005 Gravenstein Highway North, Sebastopol, CA 95472.

OReilly books may be purchased for educational, business, or sales promotional use. Online editions are also available for most titles (http://oreilly.com/safari). For more information, contact our corporate/institutional sales department: 800-998-9938 or corporate@oreilly.com .

  • Editors: Rachel Roumeliotis and Alicia Young
  • Production Editor: Kristen Brown
  • Copyeditor: Kim Cofer
  • Proofreader: James Fraleigh
  • Indexer: Judy McConville
  • Interior Designer: David Futato
  • Cover Designer: Karen Montgomery
  • Illustrator: Rebecca Demarest
  • March 2018: First Edition
Revision History for the First Edition
  • 2018-03-01: First Release

See http://oreilly.com/catalog/errata.csp?isbn=9781491980453 for release details.

The OReilly logo is a registered trademark of OReilly Media, Inc. TensorFlow for Deep Learning, the cover image, and related trade dress are trademarks of OReilly Media, Inc.

While the publisher and the authors have used good faith efforts to ensure that the information and instructions contained in this work are accurate, the publisher and the authors disclaim all responsibility for errors or omissions, including without limitation responsibility for damages resulting from the use of or reliance on this work. Use of the information and instructions contained in this work is at your own risk. If any code samples or other technology this work contains or describes is subject to open source licenses or the intellectual property rights of others, it is your responsibility to ensure that your use thereof complies with such licenses and/or rights.

978-1-491-98045-3

[M]

Preface

This book will introduce you to the fundamentals of machine learning through TensorFlow. TensorFlow is Googles new software library for deep learning that makes it straightforward for engineers to design and deploy sophisticated deep learning architectures. You will learn how to use TensorFlow to build systems capable of detecting objects in images, understanding human text, and predicting the properties of potential medicines. Furthermore, you will gain an intuitive understanding of TensorFlows potential as a system for performing tensor calculus and will learn how to use TensorFlow for tasks outside the traditional purview of machine learning.

Importantly, TensorFlow for Deep Learning is one of the first deep learning books written for practitioners. It teaches fundamental concepts through practical examples and builds understanding of machine learning foundations from the ground up. Thetarget audience for this book is practicing developers, who are comfortable with designing software systems, but not necessarily with creating learning systems. At times we use some basic linear algebra and calculus, but we will review all necessary fundamentals. We also anticipate that our book will prove useful for scientists andother professionals who are comfortable with scripting, but not necessarily with designing learning algorithms.

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.

Constant width bold

Shows commands or other text that should be typed literally by the user.

Constant width italic

Shows text that should be replaced with user-supplied values or by values determined by context.

Tip

This element signifies a tip or suggestion.

Note

This element signifies a general note.

Warning

This element indicates a warning or caution.

Using Code Examples

Supplemental material (code examples, exercises, etc.) is available for download at https://github.com/matroid/dlwithtf.

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 youre 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 OReilly 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 products documentation does require permission.

We appreciate, but do not require, attribution. An attribution usually includes the title, author, publisher, and ISBN. For example: TensorFlow for Deep Learning by Bharath Ramsundar and Reza Bosagh Zadeh (OReilly). Copyright 2018 Reza Zadeh, Bharath Ramsundar, 978-1-491-98045-3.

If you feel your use of code examples falls outside fair use or the permission given above, feel free to contact us at .

OReilly Safari
Note

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 OReilly 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 http://oreilly.com/safari.

How to Contact Us

Please address comments and questions concerning this book to the publisher:

  • OReilly Media, Inc.
  • 1005 Gravenstein Highway North
  • Sebastopol, CA 95472
  • 800-998-9938 (in the United States or Canada)
  • 707-829-0515 (international or local)
  • 707-829-0104 (fax)

We have a web page for this book, where we list errata, examples, and any additional information. You can access this page at http://bit.ly/tensorflowForDeepLearning.

To.

For more information about our books, courses, conferences, and news, see our website at http://www.oreilly.com.

Find us on Facebook: http://facebook.com/oreilly

Follow us on Twitter: http://twitter.com/oreillymedia

Watch us on YouTube: http://www.youtube.com/oreillymedia

Acknowledgments

Bharath is thankful to his PhD advisor for letting him work on this book during his nights and weekends, and especially thankful to his family for their unstinting support during the entire process.

Reza is thankful to the open source communities on which much of software and computer science is based. Open source software is one of the largest concentrations of human knowledge ever created, and this book would have been impossible without the entire community behind it.

Next page
Light

Font size:

Reset

Interval:

Bookmark:

Make

Similar books «Tensorflow for deep learning from linear regression to reinforcement learning»

Look at similar books to Tensorflow for deep learning from linear regression to reinforcement learning. We have selected literature similar in name and meaning in the hope of providing readers with more options to find new, interesting, not yet read works.


Reviews about «Tensorflow for deep learning from linear regression to reinforcement learning»

Discussion, reviews of the book Tensorflow for deep learning from linear regression to reinforcement learning and just readers' own opinions. Leave your comments, write what you think about the work, its meaning or the main characters. Specify what exactly you liked and what you didn't like, and why you think so.