Hands-On Deep Learning with TensorFlow

Dan Van Boxel

• Publisher: Packt Publishing
• Year: 2017
• ISBN:
  9781787125827
• Edition: 1
• Topic: Informatica
• Subtopic: Elaborazione di dati

This book is your guide to exploring the possibilities in the field of deep learning, making use of Google's TensorFlow. You will learn about convolutional neural networks, and logistic regression while training models for deep learning to gain key insights into your data.About This Book• Explore various possibilities with deep learning and gain amazing insights from data using Google's brainchild-- TensorFlow• Want to learn what more can be done with deep learning? Explore various neural networks with the help of this comprehensive guide• Rich in concepts, advanced guide on deep learning that will give you background to innovate in your environmentWho This Book Is ForIf you are a data scientist who performs machine learning on a regular basis, are familiar with deep neural networks, and now want to gain expertise in working with convoluted neural networks, then this book is for you. Some familiarity with C++ or Python is assumed.What You Will Learn• Set up your computing environment and install TensorFlow• Build simple TensorFlow graphs for everyday computations• Apply logistic regression for classification with TensorFlow• Design and train a multilayer neural network with TensorFlow• Intuitively understand convolutional neural networks for image recognition• Bootstrap a neural network from simple to more accurate models• See how to use TensorFlow with other types of networks• Program networks with SciKit-Flow, a high-level interface to TensorFlowIn DetailDan Van Boxel's Deep Learning with TensorFlow is based on Dan's best-selling TensorFlow video course. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible. Dan Van Boxel will be your guide to exploring the possibilities with deep learning; he will enable you to understand data like never before. With the efficiency and simplicity of TensorFlow, you will be able to process your data and gain insights that will change how you look at data.With Dan's guidance, you will dig deeper into the hidden layers of abstraction using raw data. Dan then shows you various complex algorithms for deep learning and various examples that use these deep neural networks. You will also learn how to train your machine to craft new features to make sense of deeper layers of data.In this book, Dan shares his knowledge across topics such as logistic regression, convolutional neural networks, recurrent neural networks, training deep networks, and high level interfaces. With the help of novel practical examples, you will become an ace at advanced multilayer networks, image recognition, and beyond.Style and ApproachThis book is your go-to guide to becoming a deep learning expert in your organization. Dan helps you evaluate common and not-so-common deep neural networks with the help of insightful examples that you can relate to, and show how they can be exploited in the real world with complex raw data.

Hands-On Deep Learning with TensorFlow

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Table of Contents

Hands-On Deep Learning with TensorFlow

Credits

About the Author

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Customer Feedback

Preface

What this book covers

What you need for this book

Who this book is for

Conventions

Reader feedback

Customer support

Downloading the example code

Downloading the color images of this book

Errata

Piracy

Questions

1. Getting Started

Installing TensorFlow

TensorFlow – main page

TensorFlow – the installation page

Installing via pip

Installing via CoCalc

Simple computations

Defining scalars and tensors

Computations on tensors

Doing computation

Variable tensors

Viewing and substituting intermediate values

Logistic regression model building

Introducing the font classification dataset

Logistic regression

Getting data ready

Building a TensorFlow model

Logistic regression training

Developing the loss function

Training the model

Evaluating the model accuracy

Summary

2. Deep Neural Networks

Basic neural networks

Log function

Sigmoid function

Single hidden layer model

Exploring the single hidden layer model

Backpropagation

Single hidden layer explained

Understanding weights of the model

The multiple hidden layer model

Exploring the multiple hidden layer model

Results of the multiple hidden layer

Understanding the multiple hidden layers graph

Summary

3. Convolutional Neural Networks

Convolutional layer motivation

Multiple features extracted

Convolutional layer application

Exploring the convolution layer

Pooling layer motivation

Max pooling layers

Pooling layer application

Deep CNN

Adding convolutional and pooling layer combo

CNN to classify our fonts

Deeper CNN

Adding a layer to another layer of CNN

Wrapping up deep CNN

Summary

4. Introducing Recurrent Neural Networks

Exploring RNNs

Modeling the weights

Understanding RNNs

TensorFlow learn

Setup

Logistic regression

DNNs

Convolutional Neural Networks (CNNs) in Learn

Extracting weights

Summary

5. Wrapping Up

Research evaluation

A quick review of all the models

The logistic regression model

The single hidden layer neural network model

Deep neural network

Convolutional neural network

Deep convolutional neural network

The future of TensorFlow

Some more TensorFlow projects

Summary

Index

Hands-On Deep Learning with TensorFlow

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.

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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 2017

Production reference: 1280717

Published by Packt Publishing Ltd.

Livery Place

35 Livery Street

Birmingham B3 2PB, UK.

ISBN 978-1-78728-277-3

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Credits

Author

Dan Van Boxel

Commissioning Editor

Ben Renow-Clarke

Acquisition Editor

Ben Renow-Clarke

Content Development Editor

Radhika Atitkar

Technical Editor

Bhagyashree Rai

Copy Editor

Tom Jacob

Project Coordinator

Suzanne Coutinho

Proofreader

Safis Editing

Indexer

Tejal Daruwale Soni

Graphics

Kirk D'Penha

Production Coordinator

Arvindkumar Gupta

About the Author

Dan Van Boxel is a data scientist and machine learning engineer with over 10 years of experience. He is most well-known for Dan Does Data, a YouTube livestream demonstrating the power and pitfalls of neural networks. He has developed and applied novel statistical models of machine learning to topics such as accounting for truck traffic on highways, travel time outlier detection, and other areas. Dan has also published research articles and presented findings at the Transportation Research Board and other academic journals.

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Preface

TensorFlow is an open source software library for machine learning and training neural networks. TensorFlow was originally developed by Google, and was made open source in 2015.

Over the course of this book, you will learn how to use TensorFlow to solve a novel research problem. You'll use one of the most popular machine learning approaches, neural networks with TensorFlow. We'll work on both the simple and deep neural networks to improve our models.

You'll study images of letters and digits in various fonts with the goal of identifying fonts based on one specific image of a single letter. This will be a straightforward classification problem.

As no single pixel or position—but local structures among pixels—is important, it's an ideal proble...