Deep Learning

Andrew Glassner is a research scientist specializing in computer graphics and deep learning. He is currently a Senior Research Scientist at Weta Digital, where he works on integrating deep learning with the production of world-class visual effects for films and television. He has previously worked as a researcher at labs such as the IBM Watson Lab, Xerox PARC, and Microsoft Research. He was Editor in Chief of ACM TOG, the premier research journal in graphics, and Technical Papers Chair for SIGGRAPH, the premier conference in graphics. He's written or edited a dozen technical books on computer graphics, ranging from the textbook Principles of Digital Image Synthesis to the popular Graphics Gems series, offering practical algorithms for working programmers. Glassner has a PhD in Computer Science from UNC-Chapel Hill.

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. Recurrent Neural Networks
19. Autoencoders
20. Reinforcement Learning
21. Generative Adversarial Networks
22. Creative Applications
Index