Home > Store

Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

Register your product to gain access to bonus material or receive a coupon.

Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence

Best Value Purchase

Book + eBook Bundle

  • Your Price: $53.99
  • List Price: $89.98
  • Includes EPUB, MOBI, and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

More Purchase Options

Book

  • Your Price: $39.99
  • List Price: $49.99
  • Usually ships in 24 hours.

eBook (Watermarked)

  • Your Price: $31.99
  • List Price: $39.99
  • Includes EPUB, MOBI, and PDF
  • About eBook Formats
  • This eBook includes the following formats, accessible from your Account page after purchase:

    ePub EPUB The open industry format known for its reflowable content and usability on supported mobile devices.

    MOBI MOBI The eBook format compatible with the Amazon Kindle and Amazon Kindle applications.

    Adobe Reader PDF The popular standard, used most often with the free Adobe® Reader® software.

    This eBook requires no passwords or activation to read. We customize your eBook by discreetly watermarking it with your name, making it uniquely yours.

About

Features

  • Ideal for software developers, data scientists, and analysts at all levels of experience
  • Teaches through simple visuals, accessible Python code examples, character-driven narratives, and intuitive analogies
  • Covers todays leading applications, including machine vision, natural language processing, image generation, and videogames
  • Introduces four powerful Deep Learning libraries: TensorFlow, Keras, PyTorch, and Coach
  • Carefully designed to minimize mathematical formulae and avoid unnecessary complexity
  • Ancillary resources include PowerPoint lecture slides and Instructor Notes, downloadable from the Pearson Instructor Resource Center

Description

  • Copyright 2020
  • Dimensions: 7" x 9-1/8"
  • Pages: 416
  • Edition: 1st
  • Book
  • ISBN-10: 0-13-511669-4
  • ISBN-13: 978-0-13-511669-2

Deep learning is one of today’s hottest fields. This approach to machine learning is achieving breakthrough results in some of today’s highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning Illustrated, three world-class instructors and practitioners present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn, and accessible to a far wider audience.

Part I’s high-level overview explains what Deep Learning is, why it has become so ubiquitous, and how it relates to concepts and terminology such as Artificial Intelligence, Machine Learning, Artificial Neural Networks, and Reinforcement Learning. These opening chapters are replete with vivid illustrations, easy-to-grasp analogies, and character-focused narratives.

Building on this foundation, the authors then offer a practical reference and tutorial for applying a wide spectrum of proven deep learning techniques. Essential theory is covered with as little mathematics as possible, and illuminated with hands-on Python code. Theory is supported with practical “run-throughs” available in accompanying Jupyter notebooks, delivering a pragmatic understanding of all major deep learning approaches and their applications: machine vision, natural language processing, image generation, and videogaming.

To help readers accomplish more in less time, the authors feature several of today’s most widely-used and innovative deep learning libraries, including TensorFlow and its high-level API, Keras; PyTorch, and the recently-released high-level Coach, a TensorFlow API that abstracts away the complexity typically associated with building Deep Reinforcement Learning algorithms.

Extras

Author's Site

Please visit the authors' site at deeplearningillustrated.com.

Sample Content

Online Sample Chapter

Training Deep Networks

Sample Pages

Download the sample pages (includes Chapter 8).

Table of Contents

Figures xix
Tables xxvii
Examples xxix
Foreword xxxiii
Preface xxxv
Acknowledgments xxxix
About the Authors xli


Part I: Introducing Deep Learning 1

Chapter 1: Biological and Machine Vision 3

Biological Vision 3
Machine Vision 8
TensorFlow Playground 17
Quick, Draw! 19
Summary 19

Chapter 2: Human and Machine Language 21
Deep Learning for Natural Language
Processing 21
Computational Representations of Language 25
Elements of Natural Human Language 33
Google Duplex 35
Summary 37

Chapter 3: Machine Art 39
A Boozy All-Nighter 39
Arithmetic on Fake Human Faces 41
Style Transfer: Converting Photos into Monet (and Vice Versa) 44
Make Your Own Sketches Photorealistic 45
Creating Photorealistic Images from Text 45
Image Processing Using Deep Learning 46
Summary 48

Chapter 4: Game-Playing Machines 49
Deep Learning, AI, and Other Beasts 49
Three Categories of Machine Learning Problems 53
Deep Reinforcement Learning 56
Video Games 57
Board Games 59
Manipulation of Objects 67
Popular Deep Reinforcement Learning Environments 68
Three Categories of AI 71
Summary 72

Part II: Essential Theory Illustrated 73

Chapter 5: The (Code) Cart Ahead of the (Theory)

Horse 75
Prerequisites 75
Installation 76
A Shallow Network in Keras 76
Summary 84

Chapter 6: Artificial Neurons Detecting Hot Dogs 85
Biological Neuroanatomy 101 85
The Perceptron 86
Modern Neurons and Activation Functions 91
Choosing a Neuron 96
Summary 96
Key Concepts 97

Chapter 7: Artificial Neural Networks 99
The Input Layer 99
Dense Layers 99
A Hot Dog-Detecting Dense Network 101
The Softmax Layer of a Fast Food-Classifying Network 106
Revisiting Our Shallow Network 108
Summary 110
Key Concepts 110

Chapter 8: Training Deep Networks 111
Cost Functions 111
Optimization: Learning to Minimize Cost 115
Backpropagation 124
Tuning Hidden-Layer Count and Neuron
Count 125
An Intermediate Net in Keras 127
Summary 129
Key Concepts 130

Chapter 9: Improving Deep Networks 131
Weight Initialization 131
Unstable Gradients 137
Model Generalization (Avoiding Overfitting) 140
Fancy Optimizers 145
A Deep Neural Network in
Keras 147
Regression 149
TensorBoard 152
Summary 154
Key Concepts 155

Part III: Interactive Applications of Deep Learning 157

Chapter 10: Machine Vision 159

Convolutional Neural Networks 159
Pooling Layers 169
LeNet-5 in Keras 171
AlexNet and VGGNet in Keras 176
Residual Networks 179
Applications of Machine Vision 182
Summary 193
Key Concepts 193

Chapter 11: Natural Language Processing 195
Preprocessing Natural Language Data 195
Creating Word Embeddings with word2vec 206
The Area under the ROC Curve 217
Natural Language Classification with Familiar Networks 222
Networks Designed for Sequential Data 240
Non-sequential Architectures: The Keras Functional API 251
Summary 256
Key Concepts 257

Chapter 12: Generative Adversarial Networks 259
Essential GAN Theory 259
The Quick, Draw! Dataset 263
The Discriminator Network 266
The Generator Network 269
The Adversarial Network 272
GAN Training 275
Summary 281
Key Concepts 282

Chapter 13: Deep Reinforcement Learning 283
Essential Theory of Reinforcement Learning 283
Essential Theory of Deep Q-Learning Networks 290
Defining a DQN Agent 293
Interacting with an OpenAI Gym Environment 300
Hyperparameter Optimization with SLM Lab 303
Agents Beyond DQN 306
Summary 308
Key Concepts 309

Part IV: You and AI 311

Chapter 14:  Moving Forward with Your Own Deep Learning Projects 313

Ideas for Deep Learning Projects 313
Resources for Further Projects 317
The Modeling Process, Including Hyperparameter Tuning 318
Deep Learning Libraries 321
Software 2.0 324
Approaching Artificial General Intelligence 326
Summary 328

Part V: Appendices 331

Appendix A: Formal Neural Network Notation 333

Appendix B: Backpropagation 335

Appendix C: PyTorch 339

PyTorch Features 339
PyTorch in Practice 341

Index 345

Updates

Errata

We've made every effort to ensure the accuracy of this book and its companion content. Any errors that have been confirmed since this book was published can be downloaded below.

Download the errata (17 KB .docx)

Submit Errata

More Information

InformIT Promotional Mailings & Special Offers

I would like to receive exclusive offers and hear about products from InformIT and its family of brands. I can unsubscribe at any time.

Overview


Pearson Education, Inc., 221 River Street, Hoboken, New Jersey 07030, (Pearson) presents this site to provide information about products and services that can be purchased through this site.

This privacy notice provides an overview of our commitment to privacy and describes how we collect, protect, use and share personal information collected through this site. Please note that other Pearson websites and online products and services have their own separate privacy policies.

Collection and Use of Information


To conduct business and deliver products and services, Pearson collects and uses personal information in several ways in connection with this site, including:

Questions and Inquiries

For inquiries and questions, we collect the inquiry or question, together with name, contact details (email address, phone number and mailing address) and any other additional information voluntarily submitted to us through a Contact Us form or an email. We use this information to address the inquiry and respond to the question.

Online Store

For orders and purchases placed through our online store on this site, we collect order details, name, institution name and address (if applicable), email address, phone number, shipping and billing addresses, credit/debit card information, shipping options and any instructions. We use this information to complete transactions, fulfill orders, communicate with individuals placing orders or visiting the online store, and for related purposes.

Surveys

Pearson may offer opportunities to provide feedback or participate in surveys, including surveys evaluating Pearson products, services or sites. Participation is voluntary. Pearson collects information requested in the survey questions and uses the information to evaluate, support, maintain and improve products, services or sites, develop new products and services, conduct educational research and for other purposes specified in the survey.

Contests and Drawings

Occasionally, we may sponsor a contest or drawing. Participation is optional. Pearson collects name, contact information and other information specified on the entry form for the contest or drawing to conduct the contest or drawing. Pearson may collect additional personal information from the winners of a contest or drawing in order to award the prize and for tax reporting purposes, as required by law.

Newsletters

If you have elected to receive email newsletters or promotional mailings and special offers but want to unsubscribe, simply email information@informit.com.

Service Announcements

On rare occasions it is necessary to send out a strictly service related announcement. For instance, if our service is temporarily suspended for maintenance we might send users an email. Generally, users may not opt-out of these communications, though they can deactivate their account information. However, these communications are not promotional in nature.

Customer Service

We communicate with users on a regular basis to provide requested services and in regard to issues relating to their account we reply via email or phone in accordance with the users' wishes when a user submits their information through our Contact Us form.

Other Collection and Use of Information


Application and System Logs

Pearson automatically collects log data to help ensure the delivery, availability and security of this site. Log data may include technical information about how a user or visitor connected to this site, such as browser type, type of computer/device, operating system, internet service provider and IP address. We use this information for support purposes and to monitor the health of the site, identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents and appropriately scale computing resources.

Web Analytics

Pearson may use third party web trend analytical services, including Google Analytics, to collect visitor information, such as IP addresses, browser types, referring pages, pages visited and time spent on a particular site. While these analytical services collect and report information on an anonymous basis, they may use cookies to gather web trend information. The information gathered may enable Pearson (but not the third party web trend services) to link information with application and system log data. Pearson uses this information for system administration and to identify problems, improve service, detect unauthorized access and fraudulent activity, prevent and respond to security incidents, appropriately scale computing resources and otherwise support and deliver this site and its services.

Cookies and Related Technologies

This site uses cookies and similar technologies to personalize content, measure traffic patterns, control security, track use and access of information on this site, and provide interest-based messages and advertising. Users can manage and block the use of cookies through their browser. Disabling or blocking certain cookies may limit the functionality of this site.

Do Not Track

This site currently does not respond to Do Not Track signals.

Security


Pearson uses appropriate physical, administrative and technical security measures to protect personal information from unauthorized access, use and disclosure.

Children


This site is not directed to children under the age of 13.

Marketing


Pearson may send or direct marketing communications to users, provided that

  • Pearson will not use personal information collected or processed as a K-12 school service provider for the purpose of directed or targeted advertising.
  • Such marketing is consistent with applicable law and Pearson's legal obligations.
  • Pearson will not knowingly direct or send marketing communications to an individual who has expressed a preference not to receive marketing.
  • Where required by applicable law, express or implied consent to marketing exists and has not been withdrawn.

Pearson may provide personal information to a third party service provider on a restricted basis to provide marketing solely on behalf of Pearson or an affiliate or customer for whom Pearson is a service provider. Marketing preferences may be changed at any time.

Correcting/Updating Personal Information


If a user's personally identifiable information changes (such as your postal address or email address), we provide a way to correct or update that user's personal data provided to us. This can be done on the Account page. If a user no longer desires our service and desires to delete his or her account, please contact us at customer-service@informit.com and we will process the deletion of a user's account.

Choice/Opt-out


Users can always make an informed choice as to whether they should proceed with certain services offered by InformIT. If you choose to remove yourself from our mailing list(s) simply visit the following page and uncheck any communication you no longer want to receive: www.informit.com/u.aspx.

Sale of Personal Information


Pearson does not rent or sell personal information in exchange for any payment of money.

While Pearson does not sell personal information, as defined in Nevada law, Nevada residents may email a request for no sale of their personal information to NevadaDesignatedRequest@pearson.com.

Supplemental Privacy Statement for California Residents


California residents should read our Supplemental privacy statement for California residents in conjunction with this Privacy Notice. The Supplemental privacy statement for California residents explains Pearson's commitment to comply with California law and applies to personal information of California residents collected in connection with this site and the Services.

Sharing and Disclosure


Pearson may disclose personal information, as follows:

  • As required by law.
  • With the consent of the individual (or their parent, if the individual is a minor)
  • In response to a subpoena, court order or legal process, to the extent permitted or required by law
  • To protect the security and safety of individuals, data, assets and systems, consistent with applicable law
  • In connection the sale, joint venture or other transfer of some or all of its company or assets, subject to the provisions of this Privacy Notice
  • To investigate or address actual or suspected fraud or other illegal activities
  • To exercise its legal rights, including enforcement of the Terms of Use for this site or another contract
  • To affiliated Pearson companies and other companies and organizations who perform work for Pearson and are obligated to protect the privacy of personal information consistent with this Privacy Notice
  • To a school, organization, company or government agency, where Pearson collects or processes the personal information in a school setting or on behalf of such organization, company or government agency.

Links


This web site contains links to other sites. Please be aware that we are not responsible for the privacy practices of such other sites. We encourage our users to be aware when they leave our site and to read the privacy statements of each and every web site that collects Personal Information. This privacy statement applies solely to information collected by this web site.

Requests and Contact


Please contact us about this Privacy Notice or if you have any requests or questions relating to the privacy of your personal information.

Changes to this Privacy Notice


We may revise this Privacy Notice through an updated posting. We will identify the effective date of the revision in the posting. Often, updates are made to provide greater clarity or to comply with changes in regulatory requirements. If the updates involve material changes to the collection, protection, use or disclosure of Personal Information, Pearson will provide notice of the change through a conspicuous notice on this site or other appropriate way. Continued use of the site after the effective date of a posted revision evidences acceptance. Please contact us if you have questions or concerns about the Privacy Notice or any objection to any revisions.

Last Update: November 17, 2020