About This Book
Background
1. Information Theory
2. Cross-Validation
3. Optimization for Machine Learning
4. Evaluating Predictive Distributions
Neural Networks
5. Feed-Forward Neural Networks
6. Recurrent Neural Networks
7. LSTM Networks
8. Empirical Exercise: Networks for Time Series
9. Distribution Modeling with Neural Networks
Tree-Based Methods
10. Decision Trees
11. Random Forests
12. Gradient Boosting
13. Advanced Tree-Based Methods
Further Topics
14. Advanced Hyperparameter Optimization
Data Appendix
Kernel Smoothing Primer
On this page
References
References