- 01. Introduction
- 02. Classification Problems 1
- 03. Classification Problems 2
- 04. Linear Boundaries
- 05. Higher Dimensions
- 06. Perceptrons
- 07. Why "Neural Networks"?
- 08. Perceptrons as Logical Operators
- 09. Perceptron Trick
- 10. Perceptron Algorithm
- 11. Non-Linear Regions
- 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. Perceptron vs Gradient Descent
- 25. Continuous Perceptrons
- 26. Non-linear Data
- 27. Non-Linear Models
- 28. Neural Network Architecture
- 29. Feedforward
- 30. Backpropagation
- 31. Keras
- 32. Mini Project: Students Admissions in Keras
- 33. Lesson Plan: Week 2
- 34. Training Optimization
- 35. Batch vs Stochastic Gradient Descent
- 36. Learning Rate Decay
- 37. Testing
- 38. Overfitting and Underfitting
- 39. Early Stopping
- 40. Regularization
- 41. Regularization 2
- 42. Dropout
- 43. Vanishing Gradient
- 44. Other Activation Functions
- 45. Local Minima
- 46. Random Restart
- 47. Momentum
- 48. Optimizers in Keras
- 49. Error Functions Around the World
- 50. Mini Project Intro
- 51. Mini Project: IMDB Data in Keras
- 52. Outro