Everand Logo

Welcome to Everand!

  • Discover millions of audiobooks, ebooks, and so much more with a free trial

    From $11.99/month after trial. Cancel anytime.

    Python for TensorFlow Pocket Primer: A Quick Guide to Python Libraries for TensorFlow Developers
    Python for TensorFlow Pocket Primer: A Quick Guide to Python Libraries for TensorFlow Developers
    Python for TensorFlow Pocket Primer: A Quick Guide to Python Libraries for TensorFlow Developers
    Ebook493 pages3 hours

    Python for TensorFlow Pocket Primer: A Quick Guide to Python Libraries for TensorFlow Developers

    By Mercury Learning and Information and Oswald Campesato

    ()

    Read preview
    LanguageEnglish
    PublisherPackt Publishing
    Release dateAug 12, 2024
    ISBN9781836643241
    Python for TensorFlow Pocket Primer: A Quick Guide to Python Libraries for TensorFlow Developers
    Author

    Mercury Learning and Information

    MERCURY LEARNING and INFORMATION publishes content in the areas of science and medicine, technology and computing, engineering, and mathematics designed for the professional/reference, trade, library, higher education, career school, and online training markets.

    Read more from Mercury Learning And Information

    Related to Python for TensorFlow Pocket Primer

    Related ebooks

    Programming For You

    View More
    View More

    Related podcast episodes

    Related categories

    Reviews for Python for TensorFlow Pocket Primer

    0 ratings

    0 ratings0 reviews

    What did you think?

    Tap to rate

    Review must be at least 10 words

      Book preview

      Python for TensorFlow Pocket Primer - Mercury Learning and Information

      PREFACE

      WHAT IS THE GOAL?

      The goal of this book is to show advanced beginners code samples in various Python libraries that are useful for TensorFlow programs written in Python. The first four chapters contain Python-related material, and the fifth chapter gives you an introduction to TensorFlow. However, the fifth chapter does not contain any code samples that show you how to solve machine learning problems (such as linear regression, logistic regression, and so forth) in TensorFlow.

      The material in this book is primarily for those who have some programming experience, and not really suitable for absolute beginners. It is suitable as a fast-paced introduction to various core features. The purpose of the material in the chapters is to illustrate how to solve a variety of tasks, after which you can do further reading to deepen your knowledge.

      IS THIS BOOK IS FOR ME AND WHAT WILL I LEARN?

      This book is intended for software developers who are advanced beginners (perhaps also some intermediate-level developers). You need some familiarity with working from the command line in a Unix-like environment. However, there are subjective prerequisites, such as a strong desire to learn how to write TensorFlow programs, along with the motivation and discipline to read and understand the code samples.

      If you are adequately prepared and motivated, then in Chapter 5 you will learn how to write basic TensorFlow programs.

      This book saves you the time required to search for relevant code samples, adapting them to your specific needs, which is a potentially time-consuming process. In any case, if you’re not sure whether or not you can absorb the material in this book, glance through the code samples to get a feel for the level of complexity.

      HOW WERE THE CODE SAMPLES CREATED?

      The code samples in this book were created and tested using bash on a Macbook Pro with OS X 10.12.6 (MacOS Sierra). Regarding their content: the code samples are derived primarily from the author, and in some cases there are code samples that incorporate short sections of code from discussions in online forums. The key point to remember is that the overwhelming majority of the code samples follow the Four Cs: they must be Clear, Concise, Complete, and Correct to the extent that it’s possible to do so, given the size of this book.

      Companion files for this title are available by writing to the publisher at [email protected].

      WHICH TOPICS ARE EXCLUDED?

      This book does not cover Python-related topics that are not helpful for learning the fundamentals of TensorFlow 1.x (and there are many such topics). However, there is a follow-up book that does contain code samples for machine learning in TensorFlow 1.x and Python, and the title of that book is the TensorFlow Pocket Primer.

      DO I NEED EVERYTHING IN THIS BOOK TO LEARN TENSORFLOW?

      No. There are sections in Chapter 4 (such as the material pertaining to Seaborn) that you could skip and proceed to TensorFlow. However, the extra topics are included because you will probably encounter them in the future. So, even though this book is primarily intended to prepare you for learning TensorFlow, there is some material that may be relevant if you decide to learn about machine learning with Python instead of TensorFlow.

      HOW DO I SET UP A COMMAND SHELL?

      If you are a Mac user, there are three ways to do so. The first method is to use Finder to navigate to Applications > Utilities and then double click on the Utilities application. Next, if you already have a command shell available, you can launch a new command shell by typing the following command:

      open /Applications/Utilities/Terminal.app

      A second method for Mac users is to open a new command shell on a Macbook from a command shell that is already visible simply by clicking command+n in that command shell, and your Mac will launch another command shell.

      If you are a PC user, you can install Cygwin (open source https://cygwin.com/) that simulates bash commands, or use another toolkit such as MKS (a commercial product). Please read the online documentation that describes the download and installation process. Note that custom aliases are not automatically set if they are defined in a file other than the main start-up file (such as .bash_login).

      WHAT ARE THE NEXT STEPS AFTER FINISHING THIS BOOK?

      The answer to this question varies widely, mainly because the answer depends heavily on your objectives. The best answer is to try out a new tool or technique from the book on a problem or task you care about, professionally or personally. Precisely what that might be depends on what you do, as the needs of a data scientist, manager, student, or developer are all different. In addition, keep what you learned in mind as you tackle new data cleaning or manipulation challenges. Sometimes knowing that a technique is possible, makes finding a solution easier, even if you have to re-read the section to remember exactly how the syntax

      Enjoying the preview?
      Page 1 of 1