Handbook Of Neuroevolution Through Erlang

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1. rramadassJun 2023
   Agreed, You might find Handbook of Neuroevolution Through Erlang by Gene Sher interesting.
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2. eggyJun 2023
   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/

   [2] https://github.com/phantomics/april

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3. eggySep 2022
   1. Amiga Assembly Language Programming (1987) - The book that tore me away from the Commodore BASIC I learned on my PET 2001 in 1978.

   2. The Architecture of Symbolic Computers - (1991) - I didn't read this until the 2000s, but boy, what a book! It's about Lisp and Lisp Machines, but it covers everything that is still applicable to computers. A great example of a dense, readable textbook compared to all the fluff written nowadays with too many pictures and repeated explanations.

   3. The C Programming Language, 2nd Edition - you know what I am talking about!

   4. Genetic Programming (1992)- John Koza - Really got me into early machine learning and more into LISP!

   5. The Handbook of Neuroevolution Through Erlang (2013) - Gene Sher - Great intro to ANNs in general, and Erlang even if you don't program in Erlang or want to!

   Honorable mentions:

   A. The Book of Shen - Mark Tarver - It's a LISP, but the history of computing and Lambda Calculus is nice, and Shen is an amazing language.

   B. Building High Integrity Applications with SPARK 1st Edition - Great intro to SPARK, and Ada subset, used in the book for the CubeSAT program at VTC (Vermont Technical College).

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4. rramadassMar 2021
   In case you don't already know about it; Handbook of Neuroevolution Through Erlang by Gene Sher is a great demonstration of the approved way of using Armstrong's ideas.
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5. gtycombApr 2018
   A nice application oriented book is the 'Handbook of Neuroevolution Through Erlang' by Gene I. Sher, from Springer. It has the code and a model for distributed agents (for neural networks in biology)
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6. fractallyteAug 2017
   LFE (Lisp Flavoured Erlang). It's the ideal tool for exploring AI.

   Handbook of Neuroevolution Through Erlang by Gene Sher makes a compelling argument (with detailed, varied examples) that Erlang is the perfect language with which to implement neural nets.

   Lisp has always been associated with AI, of course. Nowadays it's all Python, Java and R for machine learning, but Lisp can do just as well, plus more: Lisp has an affinity for recursion, and its homoiconicity will - I suspect - prove fundamental for true AI. One can't just 'strap on' its features to those other languages (including Elixir: https://news.ycombinator.com/item?id=7623991).

   So, obviously the right tool for the job is LFE!

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