Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence, 1st edition

Published by Addison-Wesley Professional (5 August 2019) © 2020

  • Jon Krohn
  • Grant Beyleveld
  • Aglaé Bassens
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  • Fulfilled by VitalSource
Products list

Access details

  • Instant access once purchased
  • Fulfilled by VitalSource

Title overview

Deep Learning Illustrated: A Visual, Interactive Guide to Artificial Intelligence offers an engaging and accessible introduction to one of the most transformative technologies shaping today's world. Designed for students, developers, data professionals, and anyone curious about artificial intelligence, this eText demystifies deep learning through rich visual explanations, practical examples, and an intuitive, reader-friendly approach.

Rather than focusing on complex mathematics, the authors use full-colour illustrations, real-world analogies, and interactive learning techniques to explain how deep learning systems work and why they have become the driving force behind innovations in computer vision, natural language processing, generative AI, robotics, and autonomous systems. Readers explore the relationship between artificial intelligence, machine learning, neural networks, and reinforcement learning while gaining a clear understanding of the concepts that power today's most advanced applications.

Building on this foundation, the text introduces the essential theory behind deep learning and demonstrates how modern neural networks are designed, trained, and optimised. Practical Python examples and accompanying Jupyter notebook exercises help reinforce key concepts, allowing readers to move beyond theory and develop hands-on familiarity with real-world deep learning workflows.

Covering topics such as machine vision, language models, generative adversarial networks (GANs), and deep reinforcement learning, Deep Learning Illustrated provides a comprehensive yet approachable pathway into the field. By combining visual storytelling with practical applications, it equips readers with the knowledge and confidence to understand, explore, and begin building their own deep learning solutions in an increasingly AI-driven world.

Samples

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Table of contents

  • Part I: Introducing Deep Learning
  • 1. Biological and Machine Vision
  • 2. Human and Machine Language
  • 3. Machine Art
  • 4. Game-Playing Machines
  • Part II: Essential Theory Illustrated
  • 5. The (Code) Cart Ahead of the (Theory) Horse
  • 6. Artificial Neurons Detecting Hot Dogs
  • 7. Artificial Neural Networks
  • 8. Training Deep Networks
  • 9. Improving Deep Networks
  • Part III: Interactive Applications of Deep Learning
  • 10. Machine Vision
  • 11. Natural Language Processing
  • 12. Generative Adversarial Networks
  • 13. Deep Reinforcement Learning
  • Part IV: You and AI
  • 14. Moving Forward with Your Own Deep Learning

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