- 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. Error Functions
- 19. Log-loss Error Function
- 20. Discrete vs Continuous
- 21. Softmax
- 22. One-Hot Encoding
- 23. Maximum Likelihood
- 24. Maximizing Probabilities
- 25. Cross-Entropy 1
- 26. Cross-Entropy 2
- 27. Multi-Class Cross Entropy
- 28. Logistic Regression
- 29. Gradient Descent
- 30. Gradient Descent: The Code
- 31. Perceptron vs Gradient Descent
- 32. Continuous Perceptrons
- 33. Non-linear Data
- 34. Non-Linear Models
- 35. Neural Network Architecture
- 36. Feedforward
- 37. Multilayer Perceptrons
- 38. Backpropagation
- 39. Further Reading