Let’s keep in mind that supervised learning is only one approach to ML, and backpropagation (the heaviest number crunching part of Deep Learning) is falling out of favor. DL is not very scalable, and Unsupervised Learning (i.e. Reinforcement Learning) seems to be expected to be the key path to AGI (artificial general intelligence) — according to Richard Sutton and others
With that in mind, I have been successfully using Matrex for high-dimensional vectorized computation for Multi-Armed Bandits (Elementary Reinforcement Learning). You can check it out here. Its part of The Automata Project. Down the road I may need to use python or julia via erlports (or maybe even docker containers as used here) for the vectorized parts, but for prototyping, things are going well so far.
In my view, Neuroevolutionary Typology and Weight Evolving Artificial Neural Networks (TWEANN) with Novelty Search is one of the most promising alternative approaches, and Elixir has a head start in this sub-field of ML thanks to Gene Sher’s book.
As far as python interop goes, something like this looks pretty appealing for scaling ML as well.
The Automata Project is seeking contributors if anyone here is interested.
