A totally unserious, pedagogical, functional-oriented deep learning library built on top of autograd.
You have been forewarned: this is a package that is designed to be educational, filled with experiments. Do not use it in production! You can, however, use it for a better understanding of how the internals of deep learning work.
(1) Human reinforcement learning. I aim to implement things from the deep learning world here, so that I and others can learn exactly what's going on underneath the hood.
(2) Education. This code hopefully demystifies some of the internals of deep learning libraries.
(3) Experimentation. I want to see what it takes to build a functional-first deep learning library. It is also an experiment in thinking about deep learning algorithms as functions, rather than objects. Minor pedantic detail!
(4) Compatibility with the PyData stack.
The tensor libraries out there are sometimes just frustrating to use.
We can't easily plug their tensors into matplotlib and pandas,
mainly because they do not follow Python protocols.
It is my hope that this situation rectifies over time.
Until then, this library hopefully solves that problem.
I explicitly strive to not replicate Keras, Pytorch or Chainer. If you need stability and reliability, go use those libraries instead.
If this project interests you, ways you can contribute include neural network layers (i.e. functions) layers, tests, examples, and/or docs.
If you contribute a neural network layer,
be sure to also add in docstrings and tests.
See the tests section for more details.
Tests should ensure that tensor shapes are correctly implemented.
They go inside the examples/ directory.
Docstrings should exist for each function. Because they are primarily to be rendered in an IDE (e.g. Jupyter) they should prioritize code readability rather than LaTeX rendering.