TensorFlow machine learning cookbook / Nick McClure.

Format:

Book

Author/Creator:

McClure, Nick, author.

Language:

English

Subjects (All):

Artificial intelligence--Social aspects.

Artificial intelligence.

Physical Description:

1 online resource (370 pages) : illustrations

Edition:

1st edition

Place of Publication:

Birmingham, England : Packt Publishing, 2017.

System Details:

text file

Biography/History:

McClure Nick: Nick McClure is currently a senior data scientist at PayScale, Inc. in Seattle, WA. Prior to this, he has worked at Zillow Group and Caesar's Entertainment Corporation. 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 or through his Twitter account, @nfmcclure.

Summary:

Explore machine learning concepts using the latest numerical computing library - TensorFlow - with the help of this comprehensive cookbook About This Book Your quick guide to implementing TensorFlow in your day-to-day machine learning activities Learn advanced techniques that bring more accuracy and speed to machine learning Upgrade your knowledge to the second generation of machine learning with this guide on TensorFlow Who This Book Is For This book is ideal for data scientists who are familiar with C++ or Python and perform machine learning activities on a day-to-day basis. Intermediate and advanced machine learning implementers who need a quick guide they can easily navigate will find it useful. What You Will Learn Become familiar with the basics of the TensorFlow machine learning library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks and improve predictions Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Take TensorFlow into production In Detail 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. Style and approach This book takes a recipe-based approach where every topic is explicated with the help of a real-world example.

Contents:

Cover

Copyright

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Table of Contents

Preface

Chapter 1: Getting Started with TensorFlow

Introduction

How TensorFlow Works

Declaring Tensors

Using Placeholders and Variables

Working with Matrices

Declaring Operations

Implementing Activation Functions

Working with Data Sources

Additional Resources

Chapter 2: The TensorFlow Way

Operations in a Computational Graph

Layering Nested Operations

Working with Multiple Layers

Implementing Loss Functions

Implementing Back Propagation

Working with Batch and Stochastic Training

Combining Everything Together

Evaluating Models

Chapter 3: Linear Regression

Using the Matrix Inverse Method

Implementing a Decomposition Method

Learning The TensorFlow Way of Linear Regression

Understanding Loss Functions in Linear Regression

Implementing Deming regression

Implementing Lasso and Ridge Regression

Implementing Elastic Net Regression

Implementing Logistic Regression

Chapter 4: Support Vector Machines

Working with a Linear SVM

Reduction to Linear Regression

Working with Kernels in TensorFlow

Implementing a Non-Linear SVM

Implementing a Multi-Class SVM

Chapter 5: Nearest Neighbor Methods

Working with Nearest Neighbors

Working with Text-Based Distances

Computing with Mixed Distance Functions

Using an Address Matching Example

Using Nearest Neighbors for Image Recognition

Chapter 6: Neural Networks

Implementing Operational Gates

Working with Gates and Activation Functions

Implementing a One-Layer Neural Network

Implementing Different Layers

Using a Multilayer Neural Network.

Improving the Predictions of Linear Models

Learning to Play Tic Tac Toe

Chapter 7: Natural Language Processing

Working with bag of words

Implementing TF-IDF

Working with Skip-gram Embeddings

Working with CBOW Embeddings

Making Predictions with Word2vec

Using Doc2vec for Sentiment Analysis

Chapter 8: Convolutional Neural Networks

Implementing a Simpler CNN

Implementing an Advanced CNN

Retraining Existing CNNs models

Applying Stylenet/Neural-Style

Implementing DeepDream

Chapter 9: Recurrent Neural Networks

Implementing RNN for Spam Prediction

Implementing an LSTM Model

Stacking multiple LSTM Layers

Creating Sequence-to-Sequence Models

Training a Siamese Similarity Measure

Chapter 10: Taking TensorFlow to Production

Implementing unit tests

Using Multiple Executors

Parallelizing TensorFlow

Taking TensorFlow to Production

Productionalizing TensorFlow - An Example

Chapter 11: More with TensorFlow

Visualizing graphs in Tensorboard

There's more…

Working with a Genetic Algorithm

Clustering Using K-Means

Solving a System of ODEs

Index.

Notes:

Includes index.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed March 7, 2017).

OCLC:

974929461