Introduction to Neural Networks
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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
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42. Summary
Conclusion