To better understand neural networks, I've decided to implement several from scratch.

Currently, this project contains a feedforward neural network with sigmoid activation functions, and both mean squared error and cross-entropy loss functions. Because everything is an object, adding future activation functions, loss functions, and optimization routines should be a breeze (in theory, of course; in practice, is this ever the case?).

In addition to being highly compose-able, this project is highly readable. Too often, data science code is a veritable circus of variables named "wtxb", six-times-nested for loops, and 80-line functions. The code within is both explicit and straightforward; few readers should be left wondering: "what the f*ck does that variable mean?"


Code can be found here. An example notebook is included. This is an ongoing project: I intend to add more loss functions, activation functions, convolutional and recurrent neural networks, and other optimization improvements in due time.