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> Lisp has next to no numerical libraries IIRC ?
https://github.com/CodyReichert/awesome-cl#machine-learning
https://github.com/CodyReichert/awesome-cl#numerical-and-sci...
although not necessarily bert or resnet the following probably has all the ingredients for what you are looking for. the author of this library is a research scientist at deepmind since 2015
https://github.com/melisgl/mgl#x-28MGL-BP-3A-40MGL-BP-20MGL-...
Reminds me that there was an attempt to realize the "Back to the Future" vision in Clojure.
https://github.com/incanter/incanter
It never took off but looks like there was modifications made up until three years ago.
Did you write that trading program from scratch? Or by chance did you start with:
https://github.com/wzrdsappr/trading-core
I have been considering giving this code base a spin.
I think this depends on what part of the statistics universe you're working in.
For example, within Lisp-Stat the statistics routines [1] were written by an econometrician working for the Austrian government (Julia folks might know him - Tamas Papp). It would not be exaggerating to say his job depending on it. These are state of the art, high performance algorithms, equal to anything available in R or Python. So, if you're doing econometrics, or something related, everything you need is already there in the tin.
For machine learning, there's CLML [2], developed by NTT. This is the largest telco in Japan, equivalent to ATT in the USA. As well, there is MGL [3], used to win the Higgs Boson challenge a few years back. Both actively maintained.
For linear algebra, MagicCL was mention elsewhere in the thread. My favourite is MGL-MAT [4], also by the author of MGL. This supports both BLAS and CUBLAS (CUDA for GPUs) for solutions.
Finally, there's the XLISP-STAT archive [5]. Prior to Luke Tierney, the author of XLISP-Stat joining the core R team, XLISP-STAT was the dominate statistical computing platform. There's heaps of stuff in the archive, most at least as good as what's in base R, that could be ported to Lisp-Stat.
Common Lisp is a viable platform for statistics and machine learning. It isn't (yet) quite as well organised as R or Python, but it's all there.
[1] https://github.com/Lisp-Stat/numerical-utilities/blob/master...
> his means cl pagckages can be "done".
this is true if there is nothing functional that can be added to a package. however its very much not true for ml frameworks right now. new things are being added all the time in the field. however even in the package i linked you have the necessary ingredients for any deep learning model: cuda and back propagation. the other person mentioned convolution which i think is pretty trivial to implement but still, if you expect everything for you to be ready made then you should probably stick to tf and pytorch. if you want to explore the cutting edge and push the boundaries then i think common lisp is a good tool. as an aside it might also be interesting to note that a common lisp package (Petalisp) is being used for high performance computing by a german university
https://github.com/marcoheisig/Petalisp
Quicklisp ships releases once a month, so it is very possible it didn't pick the latest release yet.
Your solution is to clone the repository into ~/quicklisp/local-projects/.
Another one would be to use the Ultralisp distribution, that ships every five minutes. https://ultralisp.org/
(ql-dist:install-dist "http://dist.ultralisp.org/"