- 01. Instructor
- 02. Introduction
- 03. Classification Problems 1
- 04. Classification Problems 2
- 05. Linear Boundaries
- 06. Higher Dimensions
- 07. Perceptrons
- 08. Why "Neural Networks"?
- 09. Perceptrons as Logical Operators
- 10. Perceptron Trick
- 11. Perceptron Algorithm
- 12. Non-Linear Regions
- 13. Error Functions
- 14. Log-loss Error Function
- 15. Discrete vs Continuous
- 16. Softmax
- 17. One-Hot Encoding
- 18. Maximum Likelihood
- 19. Maximizing Probabilities
- 20. Cross-Entropy 1
- 21. Cross-Entropy 2
- 22. Multi-Class Cross Entropy
- 23. Logistic Regression
- 24. Gradient Descent
- 25. Logistic Regression Algorithm
- 26. Pre-Lab: Gradient Descent
- 27. Notebook: Gradient Descent
- 28. Perceptron vs Gradient Descent
- 29. Continuous Perceptrons
- 30. Non-linear Data
- 31. Non-Linear Models
- 32. Neural Network Architecture
- 33. Feedforward
- 34. Backpropagation
- 35. Pre-Lab: Analyzing Student Data
- 36. Notebook: Analyzing Student Data
- 37. Outro