Viewing profile — parrt

That definitely makes it seem like it's noticing a great deal of its context window. impressive.

Wow. Cool. I have access to that model and have also seen some impressive context extraction. It also gave a really good summary of a large code base that I dumped in. I saw somebo…

The article shows how much better GPT-4o is at paying attention across its input window compared to GPT-4 Turbo and Claude-3 Sonnet. We've needed an upgrade to needle in a haystack…

BTW, how did you manage all of the throughput to the models and navigate the various throttling strategies for all the models you mentioned?

Looks really cool and useful. Seems like GPT-4o it's a lot better than 4.

Sure thing! Very enjoyable to have people use our work.

:) Yeah, I use my own internal markdown to generate really nice html (with fast latex-derived images for equations) and then full-on latex. (tool is https://github.com/parrt/bookis…

Glad to be of assistance! Yeah, It really annoyed me that this critical information was not listed in any one particular spot.

Thanks! Took me a year to discover the key nut there. L1 vs L2 regularization is not well described I found so I went nuts trying to nail it down.

Also note we recently added 1D and 2D classifier decision boundary plots. See https://github.com/parrt/dtreeviz/blob/master/notebooks/clas...

Thanks. It's morphed over time as we add functionality so it's less clean than before.

That's a good idea. thanks!

Thanks! It took forever to bash my way to victory on that trees. The lib also supports the shallow trees in boosting machines.

Looks like my ANTLR 4 reference book is available, if anybody is interested.

True, but you have to do it one at a time to look at the variables. Also, the exception message doesn't tell you which operator among several is the issue. This just makes it easie…

Thanks, Jeremy. :) I didn't go into super huge detail in the article on the implementation part as most readers won't have interest in language nerd details like you and I do.

I wondered about that. It might work just calling my internal pyviz("some python code in string) function from the debugger. It'll execute in context (maybe?)

BTW, here's a link to the twitter thread. https://twitter.com/the_antlr_guy/status/1313569854918590465

One of the biggest challenges when writing code to implement deep learning networks is getting all of the tensor (matrix and vector) dimensions to line up properly, even when using…

Thankfully pytorch does all of that calculus for us!

Glad to be of service. Yeah, I just couldn't see what RNNs were doing through all of the neural net stuff. Much easier to think of it as just gradually transforming a vector. I als…

Vanilla recurrent neural networks (RNNs) form the basis of more sophisticated models, such as LSTMs and GRUs. There are lots of great articles, books, and videos that describe the …