dfdx VS Exercism - Scala Exercises

I keep checking the progress on dfdx for this reason. It does what I (and, I assume from context, you) want: Provides static checking of tensor shapes. Which is fantastic. Not quite as much inference as I'd like but I love getting compile-time errors that I forgot to transpose before a matmul.

It depends on the generic_const_exprs feature which is still, to quote, "highly experimental":

https://github.com/rust-lang/rust/issues/76560

Definitely not for production use, but it gives a flavor for where things can head in the medium term, and it's .. it's nice. You could imagine future type support allowing even more inference for some intermediate shapes, of course, but even what it has now is really nice. Like this cute little convnet example:

https://github.com/coreylowman/dfdx/blob/main/examples/night...

And for transformers, it's really early days for dfdx, but it's a library that aims to sit basically at the Pytorch level of abstraction, that the difference is it's not just coded in Rust, but it follows the Rust-y/functional-y philosophy of "if it compiles it runs".

Wow that is super interesting. I actually tried to use GATs at first to be generic over shapes, but I couldn't do it, I'm sure it would be possible in the future though. There is this library dfdx that does something similar to what you mentioned, but it feels a little clumsy to me.

Awesome, I added an issue here https://github.com/coreylowman/dfdx/issues/597. We can discuss more there! The first step will just be adding the device and implementing tensor creation methods for it.

The next release of dfdx includes a CUDA device and implements many ops. The same dev created a new crate, cudarc, for a wrapper around CUDA toolkit.

A question I have is: what are the philosophical/design differences with dfdx? As someone who's played around with dfdx and only skimmed the README of burn, it seems like dfdx leans into Rust's type system/type inference for compile time checking of as much as is possible to check at compile time. I wonder if you've gotten a chance to look at dfdx and would like to outline what you think the differences are. Thanks!

When I got my first job as a junior software engineer, my team lead suggested I take a course by MIT, Introduction to Computer Science and Programming Using Python to improve my fundamental knowledge of computer science. The course duration was 9 weeks and I learned a lot of theory about programming and picked up Python syntax. I liked the course and especially the exercises that were presented there. At that time I also discovered an amazing website called Exercism. I thought since I became familiar with the Python syntax and knew how to build simple apps, maybe it would be nice to explore some AI-related stuff. But after playing around with it I realized AI is really not for me. I'm not into analyzing data and everything that goes with it. I was more of an engineering and problem-solving type of developer.

Exercism

Nice, this reminds me of Exercism, which I wish was more widely known since they seem to be good folks. (disclaimer, I donate to them)

https://exercism.org/

Exercism, the free programming learning platform has initiated a challenge named: 48in24.

Last year, Exercism put together the #12in23 challenge. The goal was to learn a new programming language each month throughout the year. I was one of 135 people who completed the challenge, and I learned a lot along the way!

The list of languages contains every language on Exercism, excluding ones that I've used before, web languages, or ones that I can't download for some reason.

You might like https://exercism.org/

Learning by doing, with the help of mentors. Excellent way to learn a next language (as you are already familiar with the programming concepts).

Exercism is pretty good for beginners with some programming language, they are open source and worth contributing to.

Exercism