I bought and I was working through the book, "The Handbook of Neuroevolution Through Erlang", 2013 Edition, several years ago, and then I tried to do the exerci

I bought and I was working through the book, "The Handbook of Neuroevolution Through Erlang", 2013 Edition, several years ago, and then I tried to do the exercises in LFE (Lisp Flavoured Erlang). I am more a Lisper than a Rubyist, however, I do like Elixir. Gene Sher author of the book makes a great case for Erlang and ANNs and the book even has a preface by Joe Armstrong. I played with LUSH (Lisp Universal SHell) by Yann LeCun and others[1]. I wish they would have continued with Lisp too instead of moving to Python. Personally, I am now more an APL/J fan and the language being array based suits matrix math for ANNs very well. There are some nice papers that showcase this. I'd like to see advances in APL for ML. I am currently playing with April (Array Programming Re-Imagined in Lisp)[2] to scratch this itch. It gives you the general programming of Lisp to do the regular programming and APL to do the number crunching and matrix magic in terse code. I get how array languages such as APL suit ML, and distributed languages such as those which run on the BEAM (I am now looking at Gleam, an ML-like language that runs on the BEAM. I prefer the syntax to Elixir's the same way I tried LFE first) and seem to fit ML. Python's angle is that it utilizes C/Fortran libs like LAPACK and BLAS and hooks to do what it does in ML, and Elixir's Nx is similar in that sense to get the performance in number crunching that the BEAM languages are horrible at. You can use Zig now to write NIFs for Erlang/Elixir so maybe this is how Elixir moves in the direction of Python's approach to speeding up the number crunching for ML along with Nx. I prefer to try and use a more suitable language rather than try to make it work like Python/Numba/Numpy/Pandas and Elixir/Nx do. Funny thing is that Pandas by Wes McKinney was inspired by the J programming language. So I think you may not be with the crowd in my approach, but there is some harder-to-reach fruit in this approach that is more rewarding to me, and sometimes for others. [1] https://lush.sourceforge.net/ https://lush.sourceforge.net/ [2] https://github.com/phantomics/april https://github.com/phantomics/april