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 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
- Ancillary resources include PowerPoint lecture slides and Instructor Notes, downloadable from the Pearson Instructor Resource Center
- Copyright 2020
- Pages: 416
- Edition: 1st
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EPUB (Watermarked)
- ISBN-10: 0-13-512172-8
- ISBN-13: 978-0-13-512172-6
"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 LearningDeep 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
Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Table of Contents
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Part I: Introducing Deep Learning
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1. Biological and Machine Vision
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2. Human and Machine Language
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3. Human and Machine Art
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4. Game-Playing Machines Backgammon Atari Go
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Part II: Essential Theory Illustrated
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5. Artificial Neurons
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6. Artificial Neural Networks
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7. Training Deep Networks
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8. Improving Deep Networks
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Part III: Interactive Applications
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9. Machine Vision
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10. Natural Language Processing
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11. Generative Adversarial Networks
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12. Reinforcement Learning
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Part IV: Deep Learning Libraries
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13. TensorFlow
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14. PyTorch
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Part V: Artificial Intelligence
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15. Deep Learning and the AI Revolution
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16. Building Your Own Deep Learning Project