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Additional materials
- general tips on NN
- training NN
- backprop post
- NN for any function: how it works
- post on fine tuning
- post on ADAM
- Neural networks: handwritten letters (a classic example)
- linear regression with TF
- TF classification
- cross entropy (NN)
- activation functions
- bias in NN
-
bias post
Additional lecture notes if you do not have access to a ML book and would like to check out for more:
Deep learning
Error analysis
Learning
Regularization
Perceptron
Optimization
Regularization