Apple researchers just released a paper about their LLM MM1.
As a newbie to this area, how should I break down the sections to get a better understanding of the structure of the paper?
I know the quick answer is : take all the intro courses on Deeplearning.ai & you will eventually learn all the basics. True.
I am asking is, given this (or any) LLM paper can be broken down & mapped to basic courses? So we can ramp up faster.
Any suggestions?
To give a useful answer to a question like this, it would probably require a bit more understanding of what your goals are here. There are a number of different levels at which you can approach this. Here’s what I mean, in order of increasing technical complexity:
If your goal is to understand the implications of LLMs and how their widescale deployment may affect different areas like education or typical “white collar” jobs or society in general, you can start with some of the short courses here that introduce LLMs.
The next level of complexity is to learn how to build applications on top of LLMs and ChatBots. That requires more knowledge, but there are also a number of short courses here that address those issues. Have a look at the course list.
If you want to get a job working on building and deploying LLMs as a software engineer, then you really need to just take the courses here. LLMs are definitely part of the current “state of the art”, meaning that just the beginner courses won’t get you there. LLMs are based on deep learning technologies like Sequence Models, Attention Models and Transformers. To learn about those you would need to start by taking the MLS Specialization (3 courses) as an intro and then all of the DLS Specialization (5 courses). Transformers aren’t covered until the very last week of Course 5 of DLS. Then you can follow up by taking the NLP Specialization (4 courses).
I realize I may not be answering your real question here, but I hope that the above gives at least a general picture of some possible approaches.