troye888
I’ll be starting with the andrej karpathy neural network series. Might not be reading per se, but I find it high time I actually go through and learn fully how each part of a neural net works together, instead of focusing only on small parts.
I have found it nice to use for large types (nested containers, lambdas) which are only used once, and I would not necessarily want a typedef. However I also dont like using it too much its basically trading up coding speed for reading speed. And tile and time again it has been found that the latter one is done a lot more.
I have been out of the ml world for a bit (like 6months lol …) And I already feel way out if date. It seems like I should pick up the vicuna llm, didnt want to touch llama initially due to the legal problems with it. I thought that would be a problem for a while, and then they went and solved it. Somehow even missed the news of it, most likely due to the enormous amount of news comming from the ml world (I might need a model to abbreviate it). Anyways thanks for the article I know what to do this weekend.
Back in the day before university (around 6 years ago) I got recommended a mooc(massive open online course) by the university of Helsinki. I used this course to get started with learning to program, and to find out whether it was something for me. It has been some time, and it seems they update the course but I hope it can help you too in learning. Here is the link: https://java-programming.mooc.fi/. It really starts from 0, with setting up te environment which is nice. It is in java using the netbeans ide which some would call antique, but in my opinion that does not really matter to start to learn.
The most interesting part here I find is the cost analysis. Was quite surprised to see that the cost to train it on current hardware would have been a third of the cost it was back when they were training it. That is like a 3x improvement in a year/year and a half. I winder whether this trend will continue.