Rewrite MLP colab to focus on batchnorm instead of dropout
charlesfrye opened this issue · comments
The issues with normalization are a great lead-in to introducing batchnorm, while dropout feels kinda tacked on.
Depending on timing, we could do both dropout and batchnorm -- presented as solutions to the two issues you see when you increase network depth, optimization problems and over-fitting.