Book description
Engaging projects that will teach you how complex data can be exploited to gain the most insight
About This Book
 Bored of too much theory on TensorFlow? This book is what you need! Thirteen solid projects and four examples teach you how to implement TensorFlow in production.
 This examplerich guide teaches you how to perform highly accurate and efficient numerical computing with TensorFlow
 It is a practical and methodically explained guide that allows you to apply Tensorflow's features from the very beginning.
Who This Book Is For
This book is for data analysts, data scientists, and researchers who want to increase the speed and efficiency of their machine learning activities and results. Anyone looking for a fresh guide to complex numerical computations with TensorFlow will find this an extremely helpful resource. This book is also for developers who want to implement TensorFlow in production in various scenarios. Some experience with C++ and Python is expected.
What You Will Learn
 Load, interact, dissect, process, and save complex datasets
 Solve classification and regression problems using state of the art techniques
 Predict the outcome of a simple time series using Linear Regression modeling
 Use a Logistic Regression scheme to predict the future result of a time series
 Classify images using deep neural network schemes
 Tag a set of images and detect features using a deep neural network, including a Convolutional Neural Network (CNN) layer
 Resolve character recognition problems using the Recurrent Neural Network (RNN) model
In Detail
This book of projects highlights how TensorFlow can be used in different scenarios  this includes projects for training models, machine learning, deep learning, and working with various neural networks. Each project provides exciting and insightful exercises that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors. Simply pick a project that is in line with your environment and get stacks of information on how to implement TensorFlow in production.
Style and approach
This book is a practical guide to implementing TensorFlow in production. It explores various scenarios in which you could use TensorFlow and shows you how to use it in the context of real world projects. This will not only give you an upper hand in the field, but shows the potential for innovative uses of TensorFlow in your environment. This guide opens the door to second generation machine learning and numerical computation ? a musthave for your bookshelf!
Publisher resources
Table of contents

Building Machine Learning Projects with TensorFlow
 Building Machine Learning Projects with TensorFlow
 Credits
 About the Author
 About the Reviewer
 www.PacktPub.com
 Customer Feedback
 Preface

1. Exploring and Transforming Data
 TensorFlow's main data structure  tensors
 Handling the computing workflow  TensorFlow's data flow graph
 Running our programs  Sessions
 Basic tensor methods
 Summary
 2. Clustering
 3. Linear Regression

4. Logistic Regression
 Problem description
 Logistic function predecessor  the logit functions
 The logistic function
 Example 1  univariate logistic regression
 Example 2  Univariate logistic regression with skflow
 Summary

5. Simple FeedForward Neural Networks
 Preliminary concepts
 First project  Non linear synthetic function regression
 Second project  Modeling cars fuel efficiency with non linear regression
 Third project  Learning to classify wines: Multiclass classification
 Summary

6. Convolutional Neural Networks
 Origin of convolutional neural networks
 Example 1  MNIST digit classification
 Example 2  image classification with the CIFAR10 dataset
 Summary

7. Recurrent Neural Networks and LSTM
 Recurrent neural networks
 Example 1  univariate time series prediction with energy consumption data
 Example 2  writing music "a la" Bach
 Summary
 8. Deep Neural Networks
 9. Running Models at Scale – GPU and Serving

10. Library Installation and Additional Tips

Linux installation
 Initial requirements
 Ubuntu preparation tasks (need to apply before any method)
 Pip Linux installation method
 Virtualenv installation method
 Docker installation method
 Linux installation from source
 Windows installation
 MacOS X installation
 Summary

Linux installation
Product information
 Title: Building Machine Learning Projects with TensorFlow
 Author(s):
 Release date: November 2016
 Publisher(s): Packt Publishing
 ISBN: 9781786466587
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