Deep Learning Front Cover

Deep Learning: A Visual Approach

by Andrew Glassner
June 2021, 750 pp.
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Download Chapter 3: PROBABILITY

Ever since computers began beating us at chess, they've been getting better at a wide range of human activities, from writing songs and generating news articles to helping doctors provide healthcare.

Deep learning is the source of many of these breakthroughs, and its remarkable ability to find patterns hiding in data has made it the fastest growing field in artificial intelligence (AI). Digital assistants on our phones use deep learning to understand and respond intelligently to voice commands; automotive systems use it to safely navigate road hazards; online platforms use it to deliver personalized suggestions for movies and books – the possibilities are endless.

Deep Learning: A Visual Approach is for anyone who wants to understand this fascinating field in depth, but without any of the advanced math and programming usually required to grasp its internals. If you want to know how these tools work, and use them yourself, the answers are all within these pages. And, if you’re ready to write your own programs, there are also plenty of supplemental Python notebooks in the accompanying Github repository to get you going.

The book’s conversational style, extensive color illustrations, illuminating analogies, and real-world examples expertly explain the key concepts in deep learning, including:

  • How text generators create novel stories and articles
  • How deep learning systems learn to play and win at human games
  • How image classification systems identify objects or people in a photo
  • How to think about probabilities in a way that’s useful to everyday life
  • How to use the machine learning techniques that form the core of modern AI

Intellectual adventurers of all kinds can use the powerful ideas covered in Deep Learning: A Visual Approach to build intelligent systems that help us better understand the world and everyone who lives in it. It’s the future of AI, and this book allows you to fully envision it.

Author Bio 

Dr. Andrew Glassner is a Senior Research Scientist at Weta Digital, where he uses deep learning to help artists produce visual effects for film and television. He was Technical Papers Chair for SIGGRAPH '94, Founding Editor of the Journal of Computer Graphics Tools, and Editor-in-Chief of ACM Transactions on Graphics. His prior books include the Graphics Gems series and the textbook Principles of Digital Image Synthesis. Glassner holds a PhD from UNC-Chapel Hill. He paints, plays jazz piano, and writes novels.

Table of contents 

Part I: Foundational Ideas
1. An Overview of Machine Learning Techniques
2. Essential Statistical Ideas
3. Probability
4. Bayes’ Rule
5. Curves and Surfaces
6. Information Theory
Part II: Basic Machine Learning
7. Classification
8. Training and Testing
9. Overfitting and Underfitting
10. Data Preparation
11. Classifiers
12. Ensembles
Part III: Deep Learning Basics
13. Neural Networks
14. Backpropagation
15. Optimizers
Part IV: Beyond the Basics
16. Convolutional Neural Networks
17. Convnets in Practice
18. Autoencoders
19. Recurrent Neural Networks
20. Attention and Transformers
21. Reinforcement Learning
22. Generative Adversarial Networks
23. Creative Applications
All chapters are included in this Early Access PDF.

View the detailed Table of Contents


"For a visual person like myself, Andrew's approach makes these Deep Learning concepts much more accessible than the typical algebraic treatments. Andrew is famous for his ability to teach complex topics that blend mathematics and algorithms, and this work I think is his best yet."
Peter Shirley, Distinguished Research Engineer, Nvidia

“I first came across Andrew Glassner’s first edition of Deep Learning: From Basics to Practice two years ago when I started working on the 2nd edition of my computer vision textbook. Andrew’s book was just what I needed to introduce me to this deep learning, develop my intuitions, and learn how to explain this fascinating, rapidly changing new area of computer science to others. This new updated version covers even more recent topics such as attention and transformers. The explanations and illustrations are the clearest I have found, and I would recommend that anyone entering this area, or even already familiar with the subject, read it cover-to-cover to firmly ground their understanding.“
Richard Szeliski, author of Computer Vision: Algorithms and Applications