- 01. Introducing Luis!
- 02. Intro to Neural Networks
- 03. Classification Problems 1
- 04. Classification Problems 2
- 05. Linear Boundaries
- 06. Perceptrons
- 07. Why "Neural Networks"?
- 08. Perceptrons as Logical Operators
- 09. Perceptron Trick
- 10. Perceptron Algorithm
- 11. Higher Dimensions
- 12. Error Functions
- 13. Log-loss Error Function
- 14. Discrete vs Continuous
- 15. Softmax
- 16. One-Hot Encoding
- 17. Maximum Likelihood
- 18. Maximizing Probabilities
- 19. Cross-Entropy 1
- 20. Cross-Entropy 2
- 21. Multi-Class Cross Entropy
- 22. Logistic Regression
- 23. Gradient Descent
- 24. Logistic Regression Algorithm
- 25. Non-Linear Regions
- 26. Non-Linear Models
- 27. Neural Network Architecture
- 28. Feedforward
- 29. Backpropagation
- 30. Further Reading
- 31. Neural Networks Wrap Up