paperwork

I wish JAX worked with windows natively (without using wsl). I teach a very high level intro to numpy and would _love_ to have my students try jax. These students are relatively new to programming and the idea of using a linux shell or having to compile anything themselves just wouldn't work.

I'm excited about Deno, but I'm finding that the docs still need to be improved. For example, I'm trying to build a tcp server. I'm not able to get information on how back-pressure is handled.

I can see that Deno.listen returns an object which implements reader and writer interfaces, but it isn't clear to my how to look for events, such as disconnect or that new data is available.

I wish there were examples showing how to correctly parse frames or implement protocols.

I'm sure these things will be expanded over time, partly by programmers in the community, but from the outside, things are still a bit rough.

Sounds interesting, but can’t read it because I’ve apparently exhausted the number of free medium articles can read.

Do the authors at least get some sort of micropayments from medium?

I really like the idea of being more thoughtful about naming columns and being more explicit about the “type” of data contained in them.

Is this idea already known among data modelers or data engineers?

I’d love to read any other references, if available.

Can you describe a bit more about what is going on in the project? The file you linked is over 2.5k lines of c++ code, and that is just the “setup” file. As you say, this is supposed to be a statistical model, I expected this to be R, Python or one of the standard statistical packages.

Why is there so much c++ code?

Thanks for the reply. I do have that book, but haven’t made my way to the appendix yet :)

Your explanation clears things up quite a lot.

Thanks and congrats on a useful and cool project!

I love this. The code is simple and documented. However, whenever I’ve tried to understand autograd, I get stuck at dual numbers.

As a programmer, I understand building up a computation graph where each node is some sort of an elementary function which knows how to take its own gradient. So a constant/scalar node has derivative/gradient of zero, x^n has derivative of nx^(n-1), etc. these gradients are passed from the end to the beginning according to the chain rule, etc., etc.

However, autograd is not supposed to be the symbolic differentiation we learned in high school.

This project doesn’t seem to have anything to do with duals...confused!

Those who have experience with array languages, is it fair to say that something like numpy captures most of the benefits of APL/K?

If Python allowed a reduce operator “/“, numpy will be able to get even closer to APL, correct?

I’ve seen this mentioned before, including a blog post by the fast.ai folks. Any idea where I can get details? If my tabular data set is small, what kind of embedding can I get out of it? Or is the idea that a larger data set is used for embeddings of categorical data?

Can dart be evaluated within the browser? I’ve been looking for a language which can be executed within the browser (without needing a connection to any server).

I would like to write some blog posts explaining technical concepts and provide executable, interactive code. As far as. I can tell, currently JavaScript is the only language for the job. All languages which transpile to js seem to require (out of browser) command line tools

This is highly illegal and taken very seriously. Is this speculation on your part or do you know this for a fact (in which case, you should report it).

This is pretty fantastic! I would remove the 3d effect to make it even nicer.