Machine Learning

1. Introduction to ML
2. Descending into ML
3. Reducing Loss
4. Introduction to Tensorflow
5. Generalization
6. Training and Test Sets
7. Validation Set
8. Representation
9. Feature Crosses
10. Regularization for Simplicity
11. Logistic Regression
12. Classification
13. Regularization for Sparsity
14. Neural Networks
15. Training Neural Networks
16. Multi-Class Neural Nets
17. Embeddings