Introduction to Neural Networks
Back to Home
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
Back to Home
25. Logistic Regression Algorithm
Gradient Descent Algorithm
Pause
Play
% buffered
00:00
00:00
Unmute
Mute
Disable captions
Enable captions
Settings
Captions
zh-CN
Quality
undefined
Speed
Normal
Captions
Go back to previous menu
Disabled
zh-CN
ZH-CN
en
EN
ja-JP
JA-JP
pt-BR
PT-BR
Quality
Go back to previous menu
Speed
Go back to previous menu
0.5×
0.75×
Normal
1.25×
1.5×
1.75×
2×
Exit fullscreen
Enter fullscreen
Play
Next Concept