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9780135116692

Deep Learning Illustrated A Visual, Interactive Guide to Artificial Intelligence

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  • ISBN13:

    9780135116692

  • ISBN10:

    0135116694

  • Edition: 1st
  • Format: Paperback
  • Copyright: 2019-09-18
  • Publisher: Addison-Wesley Professional

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Summary

"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, author of Wait But Why
Fully Practical, Insightful Guide to Modern Deep Learning

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
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Author Biography

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

Part I: Introducing Deep Learning
1. Biological and Machine Vision
2. Human and Machine Language
3. Human and Machine Art
4. Game-Playing Machines Backgammon Atari Go

 

Part II: Essential Theory Illustrated
5. Artificial Neurons
6. Artificial Neural Networks
7. Training Deep Networks
8. Improving Deep Networks

 

Part III: Interactive Applications
9. Machine Vision
10. Natural Language Processing
11. Generative Adversarial Networks
12. Reinforcement Learning

 

Part IV: Deep Learning Libraries
13. TensorFlow
14. PyTorch

 

Part V: Artificial Intelligence
15. Deep Learning and the AI Revolution
16. Building Your Own Deep Learning Project

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