course-content VS tinygrad

Yes. And if you want to get a jump start on what a computational neuroscience PhD might look like, check out our materials at https://compneuro.neuromatch.io. Like a PhD in a box... and all free to use/explore!

The neuromatch computational neuroscience course also seems quite interesting, though maybe less of practical use.

https://compneuro.neuromatch.io/

Recent research like "Relating transformers to models and neural representations of the hippocampal formation" might make it more relevant though (https://arxiv.org/abs/2112.04035v2)

quote from the abstract of that paper:

check out the curated datasets and project outlines for Neuromatch Academy's computational neuroscience course for some inspiration! https://compneuro.neuromatch.io

One possible thing I saw is taking https://compneuro.neuromatch.io/ courses, Although I'm not sure of the effect it adds to my CV.

https://github.com/NeuromatchAcademy/course-content https://github.com/NeuromatchAcademy/course-content-dl

If you'd like to learn more about it, you can check out last year's Comp Neuro course contents here, last year's Deep Learning course contents here, read the paper we wrote about the original NMA here, read our Nature editorial, or the Lancet article00074-0/fulltext) about us.

Check out Neuromatch Academy. If you don't have time or inclination to do the summer school this summer, you can simply access all the videos and tutorials for free at https://compneuro.neuromatch.io

https://compneuro.neuromatch.io has all the materials for free, including lecture videos and hands on Google Colab notebook programming exercises. You can also apply to take the course synchronously with a "pod" of 10 other people in a 3-week summer intensive online, with a TA to guide you and group projects to cement your understanding. To apply, check out http://academy.neuromatch.io. Applications open soon and it sounds like you'd be a great fit.

Yes, Neuromatch Academy. Their whole course is available as a Jupyter Book here

Maybe better than any single book or paper, I would recommend you check out neuromatch academy. There are 2 courses, computational neuroscience and deep learning and everything is free and open source. Play with the code, listen to the lectures, even do a "canned project" if you want experience.

Might be a silly question but is GGML a similar/competing library to George Hotz's tinygrad [0]?

[0] https://github.com/geohot/tinygrad

at first glance i thought may its like tinygrad. but looks has many ops than that tiny grad but most maps to underlying hardware provided ops?

i wonder how well tinygrad's apporach will work out, ops fusion sounds easy, just a walk a graph, pattern match it and lower to hardware provided ops?

Anyway if anyone wants to understand the philosophy behind tinygrad, this file is great start https://github.com/geohot/tinygrad/blob/master/docs/abstract...

There are currently at least 3 ways to run llama on m1 with GPU acceleration. - mlc-llm (pre-built, only 1 model has been ported) - tinygrad (very memory efficient, not that easy to integrate into other projects) - llama-mps (original llama codebase + llama adapter support)

Hopefully we will see now full support with AMD hardware on https://github.com/geohot/tinygrad. You can read more about it on https://tinygrad.org/

tinygrad

https://github.com/geohot/tinygrad/tree/master/accel/ane

But I have not tested it on Linux since Asahi has not yet added support.

llama.cpp runs at 18ms per token (7B) and 200ms per token (65B) without quantization.