Deep Learning (Adaptive Computation and Machine Learning series) - Pdf Download

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1. Introduction
2. Linear Algebra
3. Probability and Information Theory
4. Numerical Computation
5. Machine Learning Basics
6. Feedforward Deep Networks
7. Regularization
8. Optimization for Training Deep Models
9. Convolutional Networks
10. Sequence Modeling: Recurrent and Recursive Nets
11. Practical Methodology
12. Applications
13. Structured Probabilistic Models for Deep Learning
14. Monte Carlo Methods
15. Linear Factor Models and Auto-Encoders
16. Representation Learning
17. The Manifold Perspective on Representation Learning
18. Confronting the Partition Function
19. Approximate Inference
20. Deep Generative Models

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