Intro to Neural Networks

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01. Introducing Luis!
02. Intro to Neural Networks
03. Classification Problems 1
04. Classification Problems 2
05. Linear Boundaries
06. Perceptrons
07. Why "Neural Networks"?
08. Perceptrons as Logical Operators
09. Perceptron Trick
10. Perceptron Algorithm
11. Higher Dimensions
12. Error Functions
13. Log-loss Error Function
14. Discrete vs Continuous
15. Softmax
16. One-Hot Encoding
17. Maximum Likelihood
18. Maximizing Probabilities
19. Cross-Entropy 1
20. Cross-Entropy 2
21. Multi-Class Cross Entropy
22. Logistic Regression
23. Gradient Descent
24. Logistic Regression Algorithm
25. Non-Linear Regions
26. Non-Linear Models
27. Neural Network Architecture
28. Feedforward
29. Backpropagation
30. Further Reading
31. Neural Networks Wrap Up

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06. Perceptrons

DL 06 Perceptron Definition Fix V2

Given Score = 2*Test + 1*Grade - 18, suppose w₁ was 1.5 instead of 2. Would the student who got 7 on the test and 6 on the grades be accepted or rejected?

Accepted

Rejected

SOLUTION:

Rejected

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