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- Herausgeber: Packt Publishing
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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 guide
Who 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 data
In 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.
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Seitenzahl: 142
Veröffentlichungsjahr: 2016
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Table of Contents
Getting Started with TensorFlow
Getting Started with TensorFlow
Copyright © 2016 Packt Publishing
All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews.
Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without warranty, either express or implied. Neither the author, nor Packt Publishing, and its dealers and distributors will be held liable for any damages caused or alleged to be caused directly or indirectly by this book.
Packt Publishing has endeavored to provide trademark information about all of the companies and products mentioned in this book by the appropriate use of capitals. However, Packt Publishing cannot guarantee the accuracy of this information.
First published: July 2016
Production reference: 1190716
Published by Packt Publishing Ltd.
Livery Place
35 Livery Street
Birmingham
B3 2PB, UK.
ISBN 978-1-78646-857-4
www.packtpub.com
Credits
Author
Giancarlo Zaccone
Copy Editor
Alpha Singh
Reviewer
Jayani Withanawasam
Project Coordinator
Shweta H Birwatkar
Commissioning Editor
Veena Pagare
Proofreader
Safis Editing
Acquisition Editor
Vinay Argekar
Indexer
Mariammal Chettiyar
Content Development Editor
Sumeet Sawant
Production Coordinator
Nilesh Mohite
Technical Editor
Deepti Tuscano
Cover Work
Nilesh Mohite
About the Author
Giancarlo Zaccone has more than 10 years of experience managing research projects in both the scientific and industrial domains. He worked as researcher at the C.N.R, the National Research Council, where he was involved in projects related to parallel numerical computing and scientific visualization.
Currently, he is a senior software engineer at a consulting company developing and maintaining software systems for space and defence applications.
Giancarlo holds a master's degree in physics from the Federico II of Naples and a 2nd level postgraduate master course in scientific computing from La Sapienza of Rome.
He has already been a Packt author for the following book: Python Parallel Programming Cookbook.
You can contact him at https://it.linkedin.com/in/giancarlozaccone
About the Reviewer
Jayani Withanawasam is a senior software engineer at Zaizi Asia - Research and Development team. She is the author of the book Apache Mahout Essentials, on scalable machine learning. She was a summit speaker at Alfresco Summit 2014 - London. Her talk was about applications of machine learning techniques in smart enterprise content management (ECM) solutions. She presented her research “Content Extraction and Context Inference based Information Retrieval” at the Women in Machine Learning (WiML) 2015 workshop, which was co-located with the Neural Information Processing Systems (NIPS) 2015 conference - Montreal, Canada.
Jayani is currently pursuing an MSc in Artificial Intelligence at the University of Moratuwa, Sri Lanka. She has strong research interests in machine learning and computer vision.
You can contact her at https://lk.linkedin.com/in/jayaniwithanawasam
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Preface
TensorFlow is an open source software library used to implement machine learning and deep learning systems.
Behind these two names are hidden a series of powerful algorithms that share a common challenge: to allow a computer to learn how to automatically recognize complex patterns and make the smartest decisions possible.
Machine learning algorithms are supervised or unsupervised; simplifying as much as possible, we can say that the biggest difference is that in supervised learning the programmer instructs the computer how to do something, whereas in unsupervised learning the computer will learn all by itself.
Deep learning is instead a new area of machine learning research that has been introduced with the objective of moving machine learning closer to artificial intelligence goals. This means that deep learning algorithms try to operate like the human brain.
With the aim of conducting research in these fascinating areas, the Google team developed TensorFlow, which is the subject of this book.
To introduce TensorFlow’s programming features, we have used the Python programming language. Python is fun and easy to use; it is a true general-purpose language and is quickly becoming a must-have tool in the arsenal of any self-respecting programmer.
It is not the aim of this book to completely describe all TensorFlow objects and methods; instead we will introduce the important system concepts and lead you up the learning curve as fast and efficiently as we can. Each chapter of the book presents a different aspect of TensorFlow, accompanied by several programming examples that reflect typical issues of machine and deep learning.
Although it is large and complex, TensorFlow is designed to be easy to use once you learn about its basic design and programming methodology.
The purpose of Getting Started with TensorFlow is to help you do just that.
Enjoy reading!
What this book covers
Chapter 1, TensorFlow – Basic Concepts, contains general information on the structure of TensorFlow and the issues for which it was developed. It also provides the basic programming guidelines for the Python language and a first TensorFlow working session after the installation procedure. The chapter ends with a description of TensorBoard, a powerful tool for optimization and debugging.
Chapter 2, Doing Math with TensorFlow, describes the ability of mathematical processing of TensorFlow. It covers programming examples on basic algebra up to partial differential equations. Also, the basic data structure in TensorFlow, the tensor, is explained.
Chapter 3, Starting with Machine Learning, introduces some machine learning models. We start to implement the linear regression algorithm, which is concerned with modeling relationships between data. The main focus of the chapter is on solving two basic problems in machine learning; classification, that is, how to assign each new input to one of the possible given categories; and data clustering, which is the task of grouping a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups.
Chapter 4, Introducing Neural Networks, provides a quick and detailed introduction of neural networks. These are mathematical models that represent the interconnection between elements, the artificial neurons. They are mathematical constructs that to some extent mimic the properties of living neurons. Neural networks build the foundation on which rests the architecture of deep learning algorithms. Two basic types of neural nets are then implemented: the Single Layer Perceptron and the Multi Layer Perceptron for classification problems.
Chapter 5, Deep Learning, gives an overview of deep learning algorithms. Only in recent years has deep learning collected a large number of results considered unthinkable a few years ago. We’ll show how to implement two fundamental deep learning architectures, convolutional neural networks (CNN) and recurrent neural networks (RNN), for image recognition and speech translation problems respectively.
Chapter 6, GPU Programming and Serving with TensorFlow, shows the TensorFlow facilities for GPU computing and introduces TensorFlow Serving, a high-performance open source serving system for machine learning models designed for production environments and optimized for TensorFlow.
What you need for this book
All the examples have been implemented using Python version 2.7 on an Ubuntu Linux 64-bit machine, including the TensorFlow library version 0.7.1.
You will also need the following Python modules (preferably the latest version):
Who this book is for
The reader should have a basic knowledge of programming and math concepts, and at the same time, want to be introduced to the topics of machine and deep learning. After reading this book, you will be able to master TensorFlow’s features to build powerful applications.
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Questions
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Chapter 1. TensorFlow – Basic Concepts
In this chapter, we'll cover the following topics:
Machine learning and deep learning basics
Machine learning is a branch of artificial intelligence, and more specifically of computer science, which deals with the study of systems and algorithms that can learn from data, synthesizing new knowledge from them.
The word learn intuitively suggests that a system based on machine learning, may, on the basis of the observation of previously processed data, improve its knowledge in order to achieve better results in the future, or provide output closer to the desired output for that particular system.
The ability of a program or a system based on machine learning to improve its performance in a particular task, thanks to past experience, is strongly linked to its ability to recognize patterns in the data. This theme, called pattern recognition, is therefore of vital importance and of increasing interest in the context of artificial intelligence; it is the basis of all machine learning techniques.
The training of a machine learning system can be done in different ways:
