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

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


Available for Pre-Order. This item will be available on July 11, 2019

Product Details

ISBN-13: 9780135116692
Publisher: Addison-Wesley
Publication date: 07/11/2019
Series: Addison-Wesley Data & Analytics Series
Pages: 320
Product dimensions: 6.00(w) x 1.25(h) x 9.00(d)

About the Author

Jon Krohn is the chief data scientist at untapt, a machine learning startup in New York. He leads a flourishing Deep Learning Study Group, presents the acclaimed Deep Learning with TensorFlow LiveLessons in Safari, and teaches his Deep Learning curriculum at the NYC Data Science Academy. Jon holds a doctorate in neuroscience from Oxford University and has been publishing on machine learning in leading academic journals since 2010.

Grant Beyleveld is a doctoral candidate at the Icahn School of Medicine at New York’s Mount Sinai hospital, researching the relationship between viruses and their hosts. A founding member of the Deep Learning Study Group, he holds a masters in molecular medicine and medical biochemistry from the University of Witwatersrand.

Aglaé Bassens is a Belgian artist based in Brooklyn. She studied fine arts at The Ruskin School of Drawing and Fine Art, Oxford University, and University College London’s Slade School of Fine Arts. Along with her work as an illustrator, her practice includes still life painting and murals.

Table of Contents

About the Authors


Part I: Introducing Deep Learning

Chapter 1: Biological and Machine Vision

Chapter 2: Human and Machine Language

Chapter 3: Machine Art

Chapter 4: Game-Playing Machines

Part II: Essential Theory Illustrated

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

Chapter 6: Artificial Neurons Detecting Hot Dogs

Chapter 7: Artificial Neural Networks

Chapter 8: Training Deep Networks

Chapter 9: Improving Deep Networks

Part III: Interactive Applications of Deep Learning

Chapter 10: Machine Vision

Chapter 11: Natural Language Processing

Chapter 12: Generative Adversarial Networks

Chapter 13: Deep Reinforcement Learning

Part IV: Deep Learning Libraries

Chapter 14: TensorFlow

Chapter 15: PyTorch

Part V: Artificial Intelligence

Chapter 16: Building Your Own Deep Learning Project

Part VI: Appendixes

Appendix A: Formal Neural Network Notation

Appendix B: Backpropagation