Deep Learning

Author: Goodfellow | Year: 2016

Book Summary | Chapters

Chapters

• Chapter 1: Introduction
• Chapter 2: Linear Algebra
• Chapter 3: Probability
• Chapter 4: Numerical Computation
• Chapter 5: Machine Learning Basics
• Chapter 6: Deep Feedforward Networks
• Chapter 7: Regularization
• Chapter 8: Optimization
• Chapter 9: Convolutional Networks
• Chapter 10: Sequence Modeling
• Chapter 11: Practical Methodology
• Chapter 12: Applications
• Chapter 13: Linear Factor Models
• Chapter 14: Autoencoders
• Chapter 15: Representation Learning
• Chapter 16: Structured Probabilistic Models
• Chapter 17: Monte Carlo Methods
• Chapter 18: Confronting Partition Function
• Chapter 19: Approximate Inference
• Chapter 20: Deep Generative Models