Introduction to Neural Networks
Back to Home
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
Back to Home
19. Error Functions
Error Functions