I decided to try to implement everything from scratch in Elixir (after initially doing all the math with pen and paper on a trivial example to get the feel of it). Obviously pure elixir was extremely slow, so I started creating NIFs to pass over matrix multiplication to OpenBLAS. Then I was thinking more and more of what things I can pass to C code and just have Elixir as a "frontend" for it. My enthusiasm died down when I realised I was simply implementing things in C with the pretext of "doing elixir", a nice learning experience but I could see I was not doing the things that initially got me pumped up.
Don't get me wrong, I loved the discovery part of it, reading research and trying to understand so I can implement the different new (at the time) deep learning techniques, like convolutions, LSTM, and the different nuances of it. I think it gave me a better understanding of how things work and why it works. But it deviated from the initial scope and I lost interest once the learning phase was over and I knew I could simply use tensorflow or pytoorch as I did not actually need the advantages BEAM offers for this type of workload.
That pretty much describes my Tcl experience in the first Internet wave, to the point I only care for languages with good JIT/AOT tooling in the box, leaving the rest for scripting activities.
* http://neuralnetworksanddeeplearning.com/
I decided to try to implement everything from scratch in Elixir (after initially doing all the math with pen and paper on a trivial example to get the feel of it). Obviously pure elixir was extremely slow, so I started creating NIFs to pass over matrix multiplication to OpenBLAS. Then I was thinking more and more of what things I can pass to C code and just have Elixir as a "frontend" for it. My enthusiasm died down when I realised I was simply implementing things in C with the pretext of "doing elixir", a nice learning experience but I could see I was not doing the things that initially got me pumped up.
Don't get me wrong, I loved the discovery part of it, reading research and trying to understand so I can implement the different new (at the time) deep learning techniques, like convolutions, LSTM, and the different nuances of it. I think it gave me a better understanding of how things work and why it works. But it deviated from the initial scope and I lost interest once the learning phase was over and I knew I could simply use tensorflow or pytoorch as I did not actually need the advantages BEAM offers for this type of workload.
Code is still available here:
* https://gitlab.com/sdwolfz/experimental/-/tree/master/exlear...