Deep Learning Illustrated is a visual introduction to Artificial Neural Networks and AI published on Pearson's Addison-Wesley imprint in 2019. It contains comprehensive code demos and lots of hands-on, interactive content.
Deep Learning Illustrated is a visual, interactive introduction to artificial intelligence published in 2019 on Pearson's Addison-Wesley imprint. A summary of the book is available via Medium, and all of the code featured in the book is available open-source in GitHub.
Digital versions of Deep Learning Illustrated were released in August 2019 via, e.g., InformIT, Amazon Kindle, and Safari Books. Physical copies were released in September 2019, and can be pre-ordered now worldwide through, e.g., Amazon, Barnes & Noble.
If you order via this InformIT link and use the code KROHN during checkout, you get 35% off.
Deep learning is transforming software, facilitating powerful new artificial intelligence capabilities, and driving unprecedented algorithm performance. Deep Learning Illustrated is uniquely intuitive and offers a complete introduction to the discipline’s techniques. Packed with full-color figures and easy-to-follow code, it sweeps away the complexity of building deep learning models, making the subject approachable and fun to learn.
World-class instructor and practitioner Jon Krohn—with visionary content from Grant Beyleveld and beautiful illustrations by Aglaé Bassens—presents straightforward analogies to explain what deep learning is, why it has become so popular, and how it relates to other machine learning approaches. Krohn has created a practical reference and tutorial for developers, data scientists, researchers, analysts, and students who want to start applying it. He illuminates theory with hands-on Python code in accompanying Jupyter notebooks. To help you progress quickly, he focuses on the versatile deep learning library Keras to nimbly construct efficient TensorFlow models; PyTorch, the leading alternative library, is also covered.
You’ll gain a pragmatic understanding of all major deep learning approaches and their uses in applications ranging from machine vision and natural language processing to image generation and game-playing algorithms.
Discover what makes deep learning systems unique, and the implications for practitioners
Explore new tools that make deep learning models easier to build, use and improve
Master essential theory: artificial neurons, training, optimization, convolutional nets, recurrent nets, generative adversarial networks (GANs), deep reinforcement learning, and more
Walk through building interactive deep learning applications, and move forward with your own artificial intelligence projects
Jon Krohn is Chief Data Scientist at the machine learning startup untapt. He presents an acclaimed series of tutorials published by Addison-Wesley, including Deep Learning with TensorFlow and Deep Learning for Natural Language Processing. Jon teaches his deep learning curriculum in-classroom at the New York City Data Science Academy and guest lectures at Columbia University. He holds a doctorate in neuroscience from the University of Oxford and, since 2010, has been publishing on machine learning in leading peer-reviewed journals, including Advances in Neural Information Processing Systems.
Grant Beyleveld is a data scientist at untapt, where he works on natural language processing using deep learning. He holds a doctorate in biomedical science from the Icahn School of Medicine at New York City's Mount Sinai hospital, having studied the relationship between viruses and their hosts. He is a founding member of DeepLearningStudyGroup.org.
Aglaé Bassens is a Belgian artist based in Paris. 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.
“Over the next few decades, artificial intelligence is poised to dramatically change almost every aspect of our lives, in large part due to today’s breakthroughs in deep learning. The authors’ clear visual style provides a comprehensive look at what’s currently possible with artificial neural networks as well as a glimpse of the magic that’s to come.” ~ Tim Urban, writer and illustrator of Wait But Why
“This book is an approachable, practical, and broad introduction to deep learning, and the most beautifully illustrated machine learning book on the market.” ~ Dr. Michael Osborne, Dyson Associate Professor in Machine Learning, University of Oxford
“This book should be the first stop for deep learning beginners, as it contains lots of concrete, easy-to-follow examples with corresponding tutorial videos and code notebooks. Strongly recommended.” ~ Dr. Chong Li, cofounder, Nakamoto & Turing Labs; adjunct professor, Columbia University
“It’s hard to imagine developing new products today without thinking about enriching them with capabilities using machine learning. Deep learning in particular has many practical applications, and this book’s intelligible clear and visual approach is helpful to anyone who would like to understand what deep learning is and how it could impact your business and life for years to come.” ~ Helen Altshuler, engineering leader, Google
“This book leverages beautiful illustrations and amusing analogies to make the theory behind deep learning uniquely accessible. Its straightforward example code and best-practice tips empower readers to immediately apply the transformative technique to their particular niche of interest.” ~ Dr. Rasmus Rothe, founder, Merantix
“This is an invaluable resource for anyone looking to understand what deep learning is and why it powers almost every automated application today, from chatbots and voice recognition tools to self-driving cars. The illustrations and biological explanations help bring to life a complex topic and make it easier to grasp fundamental concepts.” ~ Joshua March, CEO and cofounder, Conversocial; author of Message Me
“Deep learning is regularly redefining the state of the art across machine vision, natural language, and sequential decision-making tasks. If you too would like to pass data through deep neural networks in order to build high-performance models, then this book—with its innovative, highly visual approach—is the ideal place to begin.” ~ Dr. Alex Flint, roboticist and entrepreneur