jelm | shapeRank | |
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3 | 2 | |
12 | 43 | |
- | - | |
10.0 | 0.0 | |
almost 5 years ago | over 2 years ago | |
TeX | TypeScript | |
- | Apache License 2.0 |
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jelm
- Is APL Dead?
- The Lisp OS “Mezzano” Running Native on Librebooted ThinkPads
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Learning Common Lisp to beat Java and Rust on a phone encoding problem
I have a bunch of links to ML material for either APL or J. I don't know of any particular library for J. J is interpreted, so it is not as fast as other implementations. I am mainly using it to experiment on concepts and teach myself more ML in J because of the iterative nature of the REPL, and the succinct code. I can keep what's going on in my head, and glance at less than 100 lines, usually 15 lines, of code to refresh it.
There is a series of videos of learning neural networks in APL cited by others here on this thread.
Pandas author, Wes McKinney, cited J as an influence in his work on Pandas.
Extreme Learning Machine in J (code and PDF are here too):
https://github.com/peportier/jelm
Convolutional neural networks in APL (PDF and video on page):
https://dl.acm.org/doi/10.1145/3315454.3329960
A DSL to implement MENACE (Matchbox Educable Noughts And Crosses Engine) in APL (Noughts and Crosses or Tic-tac-toe):
https://romilly.github.io/o-x-o/an-introduction.html
shapeRank
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Is APL Dead?
Obviously this is relevant to machine learning and data analytics. On github: https://github.com/f5devcentral/shapeRank
- ShapeRank
What are some alternatives?
BQN - An APL-like programming language. Self-hosted!
array - Simple array language written in kotlin
apltail - APL Compiler targeting a typed array intermediate language
woo - A fast non-blocking HTTP server on top of libev
j-prez
bordeaux-threads - Portable shared-state concurrency for Common Lisp
prechelt-phone-number-encoding - Comparison between Java and Common Lisp solutions to a phone-encoding problem described by Prechelt
ExpansionCards - Reference designs and documentation to create Expansion Cards for the Framework Laptop