Introduction to Neural Networks
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01. Instructor
02. Introduction
03. Classification Problems 1
04. Classification Problems 2
05. Linear Boundaries
06. Higher Dimensions
07. Perceptrons
08. Why "Neural Networks"?
09. Perceptrons as Logical Operators
10. Perceptron Trick
11. Perceptron Algorithm
12. Non-Linear Regions
13. Error Functions
14. Log-loss Error Function
15. Discrete vs Continuous
16. Softmax
17. One-Hot Encoding
18. Maximum Likelihood
19. Maximizing Probabilities
20. Cross-Entropy 1
21. Cross-Entropy 2
22. Multi-Class Cross Entropy
23. Logistic Regression
24. Gradient Descent
25. Logistic Regression Algorithm
26. Pre-Lab: Gradient Descent
27. Notebook: Gradient Descent
28. Perceptron vs Gradient Descent
29. Continuous Perceptrons
30. Non-linear Data
31. Non-Linear Models
32. Neural Network Architecture
33. Feedforward
34. Backpropagation
35. Pre-Lab: Analyzing Student Data
36. Notebook: Analyzing Student Data
37. Outro
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36. Notebook: Analyzing Student Data
Code
If you need a code on the
https://github.com/udacity
.
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