ML Session n°4

Transcript

1. ML: regularization and neural networks March 2017
2. Recap
3. Recap: linear regression
4. Recap: polynomial regression
5. Recap: gradient descent
6. Regularization
7. Normalization Goal : have all features be equivalent (in size) Rescaling : Standardization :
8. Regularization : goal
9. Regularization Before : Now : Forces values to stay low
10. Neural networks
11. Demo: Tensorflow Playground
12. Biological neuron
13. Artificial neuron: Perceptron
14. Teaching a neuron
15. Artifical neuron : activation function
16. Most common activation functions Identity Sigmoid Tanh ReLU
17. Teaching a neuron
18. Demo: simple network, but the hard way (92%)
19. Demo: simple network, but the hard way (92%)
20. Artificial neuron -> Artificial neural network
21. Artificial neuron -> Artificial neural network
22. Teaching a neural net
23. Demo: Tensorflow Playground
24. Demo: layered network, still the hard way (97%)
25. Questions? March 2017