deep-diamond VS tech.ml

I had some "classical ML" knowledge and knew a bit about the math behind DL and tensors in general thanks to the book Deep Learning for Programmers showcased in this repo: https://github.com/uncomplicate/deep-diamond (it's not in Rust, and I'm not sure what the current state of it is, though!).

Plase check out clojars, or https://neanderthal.uncomplicate.org/ https://clojurecuda.uncomplicate.org/ https://github.com/uncomplicate/deep-diamond

Do clojure as a side gig or in free time. Let day job pay the bills. If you can, maybe incorporate clojure into work job to solve small problems (https://github.com/clj-python/libpython-clj and https://github.com/scicloj/clojisr provide bridges to/from python and r). There is a lot of effort going into the data science side as well; the scicloj effort has resulted in a lot of growth over the last 2 years. tech.ml.dataset, tech.ml (now scicloj.ml). Dragan has a bunch of excellent stuff in neanderthal and deep diamond. There are also bindings to other jvm libraries from multiple languages.

For ml, there's a lot of work going on integrating stuff from various ecosystems (java, scala, clojure). tech.ml is the original entry in this space, and is being worked with to merge with some other efforts, mainly around ML pipelines akin to sklearn.