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. 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
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02. Introduction to Deep Learning
Module Introduction
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