Getting Started with TensorFlow

by Zaccone, Giancarlo

Type Book

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Title Statements

Full title :Getting Started with TensorFlow.

Summary

Get up and running with the latest numerical computing library by Google and dive deeper into your data!About This Book Get the first book on the market that shows you the key aspects TensorFlow, how it works, and how to use it for the second generation of machine learning Want to perform faster and more accurate computations in the field of data science? This book will acquaint you with an all-new refreshing library--TensorFlow! Dive into the next generation of numerical computing and get the most out of your data with this quick guideWho This Book Is For This book is dedicated to all the machine learning and deep learning enthusiasts, data scientists, researchers, and even students who want to perform more accurate, fast machine learning operations with TensorFlow. Those with basic knowledge of programming (Python and C/C++) and math concepts who want to be introduced to the topics of machine learning will find this book useful. What You Will Learn Install and adopt TensorFlow in your Python environment to solve mathematical problems Get to know the basic machine and deep learning concepts Train and test neural networks to fit your data model Make predictions using regression algorithms Analyze your data with a clustering procedure Develop algorithms for clustering and data classification Use GPU computing to analyze big dataIn Detail Google's TensorFlow engine, after much fanfare, has evolved in to a robust, user-friendly, and customizable, application-grade software library of machine learning (ML) code for numerical computation and neural networks. This book takes you through the practical software implementation of various machine learning techniques with TensorFlow. In the first few chapters, you'll gain familiarity with the framework and perform the mathematical operations required for data analysis. As you progress further, you'll learn to implement various machine learning techniques such as classification, clustering, neural networks, and deep learning through practical examples. By the end of this book, you'll have gained hands-on experience of using TensorFlow and building classification, image recognition systems, language processing, and information retrieving systems for your application. Style and approach Get quickly up and running with TensorFlow using this fast-paced guide. You will get to know everything that can be done with TensorFlow and we'll show you how to implement it in your environment. The examples in the book are from the core of the computation industry--something you can connect to and will find familiar.

Edition Statements

Publication Date:2016.

Publication Statements

Publisher(s):Packt Publishing
Place of Publication:Birmingham
Language:eng
Publication Date:2016.

Notes

How to install TensorFlow Serving.

The Physical Object

Description:1 online resource (178 pages)

Edition Identifiers

ISBN:9781786469069, 1786469065
oclc:ocn974583642
LC Classification:Q325.5
Dewey Decimal:006.31

Cover; Copyright; Credits; About the Author; About the Reviewer; www.PacktPub.com; Table of Contents; Preface; Chapter 1: TensorFlow -- Basic Concepts; Machine learning and deep learning basics; Supervised learning; Unsupervised learning; Deep learning; TensorFlow -- A general overview; Python basics; Syntax; Data types; Strings; Control flow; Functions; Classes; Exceptions; Importing a library; Installing TensorFlow; Installing on Mac or Linux distributions; Installing on Windows; Installation from source; Testing your TensorFlow installation; First working session; Data Flow Graphs.

TensorFlow programming modelHow to use TensorBoard; Summary; Chapter 2: Doing Math with TensorFlow; The tensor data structure; One-dimensional tensors; Two-dimensional tensors; Tensor handling; Three-dimensional tensors; Handling tensors with TensorFlow; Prepare the input data; Complex numbers and fractals; Prepare the data for Mandelbrot set; Build and execute the Data Flow Graph for Mandelbrot's set; Visualize the result for Mandelbrot's set; Prepare the data for Julia's set; Build and execute the Data Flow Graph for Julia's set; Visualize the result; Computing gradients; Random numbers.

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Subjects

Apprentissage automatique artificial intelligence Data Processing; Artificial Intelligenc… Electronic books Intelligence artificielle Machine learning TensorFlow (electronic resource)