This is a dumb simple naive implementation of TensorFlow like framework for matrices.
I implemented it as a tiny toy project to learn how a back-propagation work.
I tried to keep the code as Scala-ish as possible.
- caching forward pass
- parallelization (SDG)
- probabilistic programming (inspiration)
Those are just two must have ideas (for me to not to forget):
- the forward pass should be run only once and cached for the backward pass to be efficient.
- create interface for
Opsuch that an explicit function for derivatives of all parameters can be specified - might be useful when implementing more sophisticated numerical methods...
- ...