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- Herausgeber: Packt Publishing
- Kategorie: Fachliteratur
- Sprache: Englisch
TensorFlow is an open source software library for Machine Intelligence. The independent recipes in this book will teach you how to use TensorFlow for complex data computations and will let you dig deeper and gain more insights into your data than ever before. You’ll work through recipes on training models, model evaluation, sentiment analysis, regression analysis, clustering analysis, artificial neural networks, and deep learning – each using Google’s machine learning library TensorFlow.
This guide starts with the fundamentals of the TensorFlow library which includes variables, matrices, and various data sources. Moving ahead, you will get hands-on experience with Linear Regression techniques with TensorFlow. The next chapters cover important high-level concepts such as neural networks, CNN, RNN, and NLP.
Once you are familiar and comfortable with the TensorFlow ecosystem, the last chapter will show you how to take it to production.
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Veröffentlichungsjahr: 2017
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Table of Contents
TensorFlow Machine Learning Cookbook
TensorFlow Machine Learning Cookbook
Copyright © 2017 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: February 2017
Production reference: 1090217
Published by Packt Publishing Ltd.
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Birmingham B3 2PB, UK.
ISBN 978-1-78646-216-9
www.packtpub.com
Credits
Author
Nick McClure
Reviewer
Chetan Khatri
Commissioning Editor
Veena Pagare
Acquisition Editor
Manish Nainani
Content Development Editor
Sumeet Sawant
Technical Editor
Akash Patel
Copy Editor
Safis Editing
Project Coordinator
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Proofreader
Safis Editing
Indexer
Mariammal Chettiyar
Graphics
Disha Haria
Production Coordinator
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Cover Work
Arvindkumar Gupta
About the Author
Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow and Caesar's Entertainment. He got his degrees in Applied Mathematics from The University of Montana and the College of Saint Benedict and Saint John's University.
He has a passion for learning and advocating for analytics, machine learning, and artificial intelligence. Nick occasionally puts his thoughts and musings on his blog, http://fromdata.org/, or through his Twitter account, @nfmcclure.
I am very grateful to my parents, who have always encouraged me to pursue knowledge. I also want to thank my friends and partner, who have endured my long monologues about the subjects in this book and always have been encouraging and listening to me. Writing this book was made easier by the amazing efforts of the open source community and the great documentation of many projects out there related to TensorFlow.
A special thanks goes out to the TensorFlow developers at Google. Their great product and skill speaks volumes for itself, and is accompanied by great documentation, tutorials, and examples.
About the Reviewer
Chetan Khatri is a Data Science Researcher with a total of 5 years of experience in research and development. He works as a Lead – Technology at Accionlabs India. Prior to that he worked with Nazara Games where he was leading Data Science practice as a Principal Big Data Engineer for Gaming and Telecom Business. He has worked with leading data companies and a Big 4 companies, where he has managed the Data Science Practice Platform and one of the Big 4 company's resources teams.
He completed his master's degree in computer science and minor data science at KSKV Kachchh University and awarded a "Gold Medalist" by the Governer of Gujarat for his "University 1st Rank" achievements.
He contributes to society in various ways, including giving talks to sophomore students at universities and giving talks on the various fields of data science, machine learning, AI, and IoT in academia and at various conferences. He has excellent correlative knowledge of both academic research and industry best practices. Hence, he always comes forward to remove the gap between Industry and Academia, where he has good number of achievements. He is the co-author of various courses, such as Data Science, IoT, Machine Learning/AI, and Distributed Databases in PG/UG cariculla at University of Kachchh. Hence, University of Kachchh became first government university in Gujarat to introduce Python as the first programming language in Cariculla and India's first government university to introduce Data Science, AI, and IoT courses in cariculla entire success story presented by Chetan at Pycon India 2016 conference. He is one of the founding members of PyKutch—A Python Community.
Currently, he is working on Intelligent IoT Devices with Deep Learning , Reinforcement learning and Distributed computing with various modern architectures.
I would like to thanks Prof. Devji Chhanga, head of the Computer Science Department, University of Kachchh, for guiding me to the correct path and for his valuable guidance in the field of data science research.
I would also like to thanks Prof. Shweta Gorania for being the first to introduce Genetic Algorithms and Neural Networks.
Last but not least I would like to thank my beloved family for their support.
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Preface
TensorFlow was open sourced in November of 2015 by Google, and since then it has become the most starred machine learning repository on GitHub. TensorFlow's popularity is due to the approach of creating computational graphs, automatic differentiation, and customizability. Because of these features, TensorFlow is a very powerful and adaptable tool that can be used to solve many different machine learning problems.
This book addresses many machine learning algorithms, applies them to real situations and data, and shows how to interpret the results.
What this book covers
Chapter 1, Getting Started with TensorFlow, covers the main objects and concepts in TensorFlow. We introduce tensors, variables, and placeholders. We also show how to work with matrices and various mathematical operations in TensorFlow. At the end of the chapter we show how to access the data sources used in the rest of the book.
Chapter 2, The TensorFlow Way, establishes how to connect all the algorithm components from Chapter 1 into a computational graph in multiple ways to create a simple classifier. Along the way, we cover computational graphs, loss functions, back propagation, and training with data.
Chapter 3, Linear Regression, focuses on using TensorFlow for exploring various linear regression techniques, such as Deming, lasso, ridge, elastic net, and logistic regression. We show how to implement each in a TensorFlow computational graph.
Chapter 4, Support Vector Machines, introduces support vector machines (SVMs) and shows how to use TensorFlow to implement linear SVMs, non-linear SVMs, and multi-class SVMs.
Chapter 5, Nearest Neighbor Methods, shows how to implement nearest neighbor techniques using numerical metrics, text metrics, and scaled distance functions. We use nearest neighbor techniques to perform record matching among addresses and to classify hand-written digits from the MNIST database.
Chapter 6, Neural Networks, covers how to implement neural networks in TensorFlow, starting with the operational gates and activation function concepts. We then show a shallow neural network and show how to build up various different types of layers. We end the chapter by teaching TensorFlow to play tic-tac-toe via a neural network method.
Chapter 7, Natural Language Processing, illustrates various text processing techniques with TensorFlow. We show how to implement the bag-of-words technique and TF-IDF for text. We then introduce neural network text representations with CBOW and skip-gram and use these techniques for Word2Vec and Doc2Vec for making real-world predictions.
Chapter 8, Convolutional Neural Networks, expands our knowledge of neural networks by illustrating how to use neural networks on images with convolutional neural networks (CNNs). We show how to build a simple CNN for MNIST digit recognition and extend it to color images in the CIFAR-10 task. We also illustrate how to extend prior trained image recognition models for custom tasks. We end the chapter by explaining and showing the stylenet/neural style and deep-dream algorithms in TensorFlow.
Chapter 9, Recurrent Neural Networks, explains how to implement recurrent neural networks (RNNs) in TensorFlow. We show how to do text-spam prediction, and expand the RNN model to do text generation based on Shakespeare. We also train a sequence to sequence model for German-English translation. We finish the chapter by showing the usage of Siamese RNN networks for record matching on addresses.
Chapter 10, Taking TensorFlow to Production, gives tips and examples on moving TensorFlow to a production environment and how to take advantage of multiple processing devices (for example GPUs) and setting up TensorFlow distributed on multiple machines.
Chapter 11, More with TensorFlow, show the versatility of TensorFlow by illustrating how to do k-means, genetic algorithms, and solve a system of ordinary differential equations (ODEs). We also show the various uses of Tensorboard, and how to view computational graph metrics.
What you need for this book
The recipes in this book use TensorFlow, which is available at https://www.tensorflow.org/ and are based on Python 3, available at https://www.python.org/downloads/. Most of the recipes will require the use of an Internet connection to download the necessary data.
Who this book is for
The TensorFlow Machine Learning Cookbook is for users that have some experience with machine learning and some experience with Python programming. Users with an extensive machine learning background may find the TensorFlow code enlightening, and users with an extensive Python programming background may find the explanations helpful.
Sections
In this book, you will find several headings that appear frequently (Getting ready, How to do it…, How it works…, There's more…, and See also).
To give clear instructions on how to complete a recipe, we use these sections as follows:
Getting ready
This section tells you what to expect in the recipe, and describes how to set up any software or any preliminary settings required for the recipe.
How to do it…
This section contains the steps required to follow the recipe.
How it works…
This section usually consists of a detailed explanation of what happened in the previous section.
There's more…
This section consists of additional information about the recipe in order to make the reader more knowledgeable about the recipe.
See also
This section provides helpful links to other useful information for the recipe.
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Chapter 1. Getting Started with TensorFlow
In this chapter, we will cover basic recipes in order to understand how TensorFlow works and how to access data for this book and additional resources. By the end of the chapter, you should have knowledge of the following:
Introduction
Google's TensorFlow engine has a unique way of solving problems. This unique way allows us to solve machine learning problems very efficiently. Machine learning is used in almost all areas of life and work, but some of the more famous areas are computer vision, speech recognition, language translations, and healthcare. We will cover the basic steps to understand how TensorFlow operates and eventually build up to production code techniques later in the book. These fundamentals are important in order to understand the recipes in the rest of this book.
