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. • 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 today’s 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
Figures
Tables
Examples
Foreword
Preface
Acknowledgments
About the Authors
Part I: Introducing Deep Learning 1
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)
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: You and AI
Chapter 14: Moving Forward with Your Own Deep Learning Projects
Part V: Appendices
Appendix A: Formal Neural Network Notation
Appendix B: Backpropagation
Appendix C: PyTorch
Index 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.
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