Notes
Loss for an example is just the negative of the logprob that the model assigned to the true class?
Error bars as degrees of belief
Reasoning about neural network training with bias-variance tradeoff
Information theory and logistic regression both arrive at cross-entropy
Equivariance and invariance explain so much of deep learning
Why not initialize neural network weight to zero?