- 01. Neural Network Intuition
- 02. Introduction to Deep Learning
- 03. Starting Machine Learning
- 04. A Note on Deep Learning
- 05. Quiz: Housing Prices
- 06. Solution: Housing Prices
- 07. Linear to Logistic Regression
- 08. Classification Problems 1
- 09. Classification Problems 2
- 10. Linear Boundaries
- 11. Higher Dimensions
- 12. Perceptrons
- 13. Perceptrons II
- 14. Why "Neural Networks"?
- 15. Perceptrons as Logical Operators
- 16. Perceptron Trick
- 17. Perceptron Algorithm
- 18. Non-Linear Regions
- 19. Error Functions
- 20. Log-loss Error Function
- 21. Discrete vs Continuous
- 22. Softmax
- 23. One-Hot Encoding
- 24. Maximum Likelihood
- 25. Maximizing Probabilities
- 26. Cross-Entropy 1
- 27. Cross-Entropy 2
- 28. Multi-Class Cross Entropy
- 29. Logistic Regression
- 30. Gradient Descent
- 31. Gradient Descent: The Code
- 32. Perceptron vs Gradient Descent
- 33. Continuous Perceptrons
- 34. Non-linear Data
- 35. Non-Linear Models
- 36. Neural Network Architecture
- 37. Feedforward
- 38. Multilayer Perceptrons
- 39. Backpropagation
- 40. Further Reading
- 41. Create Your Own NN
- 42. Summary