Neural Networks
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01. Introducing Luis
02. Why "Neural Networks"?
03. Neural Network Architecture
04. Feedforward
05. Backpropagation
06. Training Optimization
07. Testing
08. Overfitting and Underfitting
09. Early Stopping
10. Regularization
11. Regularization 2
12. Dropout
13. Local Minima
14. Vanishing Gradient
15. Other Activation Functions
16. Batch vs Stochastic Gradient Descent
17. Learning Rate Decay
18. Random Restart
19. Momentum
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10. Regularization
Regularization
DL 53 Q Regularization
Which gives a smaller error?
x1 + x2
10x1 + 10x2
SOLUTION:
10x1 + 10x2
