The problem with LLM courses is that the topic is mostly alchemy and will not bring you much real enlightenment.
inigyou 6 hours ago [-]
It's alchemy that works and if you don't know it you are left behind, so that's important.
Planktonne 6 hours ago [-]
> if you don't know it you are left behind
This simply isn't true. Given that the whole promise of AI is accessibility, there isn't an obstacle being raised by other people adopting it faster. You can always pick up the latest trick quickly, and if you struggle at all, an LLM can explain. There is no evidence of people falling behind.
The idea that you need specialised knowledge to compete with the tool that is designed to let you do things without specialised knowledge is trivially nonsensical.
4 hours ago [-]
krige 6 hours ago [-]
only for a given value of works
CamperBob2 2 hours ago [-]
"Gets shit done" is a good-enough definition of "works" in most economically-valuable contexts.
Did I read that right? Or is it more 5 hours of instructional videos?
Either way, it doesn't seem to include grading or other help etc.
great_wubwub 19 hours ago [-]
Yes but you get a digital badge with it, so that's nice.
ksd482 19 hours ago [-]
I am not sure if you are being sarcastic because I don't know how people view IEEE "digital badges", but anything from MOOCs on LinkedIn stopped being valuable a long time ago, if it ever was.
great_wubwub 3 hours ago [-]
Oh that was very much sarcasm. I have no idea why I'd pay for five hours of recorded videos from two guys from Samsung when I can get top-tier academic and industry content for free. I'm happy to pay for good education but nothing about this training has $240 of value to me.
handfuloflight 17 hours ago [-]
Better spent on a $200 OpenAI or Anthropic sub and having their top models give you instant, personalized teaching.
CamperBob2 12 hours ago [-]
I like how the correct, optimal suggestion is being downvoted by people who've never tried it, or who last tried it in 2022.
Suggest going through the papers (or the subset that interests you) listed at https://news.ycombinator.com/item?id=48822131 with a GPT/Claude/Gemini chat window open. Supplement with the Karpathy 'Zero to Hero' video series if it suits your preferred learning style. That will get you where you want to be, in terms of ML knowledge. It won't get you a job at Anthropic, but neither will a paid IEEE course.
BlocksAI 6 hours ago [-]
LLM training courses may have some valuable tips and tricks behind them, but the platforms change so often and no two personalized LLMs look the same. It feels like prompting isn't a science you can capture with step-by-step tutorials, but rather it's an art form you compose. Can start from the same place and get two completely different outcomes.
I didn’t realize IEEE had courses. I’m curious if anyone can comment on the general quality and if they have any good ones.
sgt101 10 hours ago [-]
>>Relying on such LLMs without understanding their internal logic creates a significant reliability risk. To build tools that work consistently, developers must understand the core principles that govern how the models process information and generate results. By mastering how a model processes information and how its internal settings influence the result, developers can move away from a trial-and-error approach toward a more precise one to ensure the AI tool handles complex data reliably.
This is staggering bullshitp. In what way does understanding a transformer allow you to solve the core problem of LLM's that no frontier lab has managed to resolve?
>>To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a company’s database.
This also is bullshit. Yes, RAG helps and reduces errors, but NOOOO! it does not fix hallucinations...
>>Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up “private” instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.
This is somewhat true, but really the motive is providing a soverign instance that cannot be withdrawn for arbitary reasons. Fundamentally the big providers are not going to steal your data, they may change the license to allow them to use it in the future, but then all their big customers will leave. So, they won't be able to, probably. What might well happen (and has happened) is that the USA might withdraw access with no notice leaving you high and dry.
I want to learn to build a real LLM so I looked at https://allenai.org/olmo where there are instructions and ingredients. But, unfortunately I can't afford the required compute resource so I will have to wait for a bit I guess.
karahime 6 hours ago [-]
Personally, I think understanding deeply how a transformer works helps a lot to understand what's probably the result of specific choices in the RL process vs what's architecture. A lot of the "We asked 30 LLMs and they all said the same thing" type analyses of how LLMs work often bump into what's being prioritized in the name of alignment right now, as opposed to architectural insights.
andrewstuart 4 hours ago [-]
I really want to read a bunch of old papers from the IEE library but it looks very hard to get access to at any reasonable price for personal usage.
Anyone got any hints or tips for me?
CamperBob2 2 hours ago [-]
Search for the DOI string that is associated with virtually every paper (which often but not always looks like a URL) and paste it into scihub.ru. If you are primarily interested in older papers (pre-2022 or so) this will be the path of least resistance, or at least minimum loop area.
black_13 12 hours ago [-]
[dead]
Rendered at 17:52:45 GMT+0000 (Coordinated Universal Time) with Vercel.
This simply isn't true. Given that the whole promise of AI is accessibility, there isn't an obstacle being raised by other people adopting it faster. You can always pick up the latest trick quickly, and if you struggle at all, an LLM can explain. There is no evidence of people falling behind.
The idea that you need specialised knowledge to compete with the tool that is designed to let you do things without specialised knowledge is trivially nonsensical.
$240 (non member price) for a 5 hour course.
Did I read that right? Or is it more 5 hours of instructional videos?
Either way, it doesn't seem to include grading or other help etc.
Suggest going through the papers (or the subset that interests you) listed at https://news.ycombinator.com/item?id=48822131 with a GPT/Claude/Gemini chat window open. Supplement with the Karpathy 'Zero to Hero' video series if it suits your preferred learning style. That will get you where you want to be, in terms of ML knowledge. It won't get you a job at Anthropic, but neither will a paid IEEE course.
This is staggering bullshitp. In what way does understanding a transformer allow you to solve the core problem of LLM's that no frontier lab has managed to resolve?
>>To fix the problem, retrieval-augmented generation (RAG) forces AI to look up information in a trusted source such as a company’s database.
This also is bullshit. Yes, RAG helps and reduces errors, but NOOOO! it does not fix hallucinations...
>>Prioritizing data security. When using AI with proprietary code, security is a major concern. Engineers must learn how to set up “private” instances of the models to ensure that sensitive company data stays within a secure cloud environment and is not used to train public versions.
This is somewhat true, but really the motive is providing a soverign instance that cannot be withdrawn for arbitary reasons. Fundamentally the big providers are not going to steal your data, they may change the license to allow them to use it in the future, but then all their big customers will leave. So, they won't be able to, probably. What might well happen (and has happened) is that the USA might withdraw access with no notice leaving you high and dry.
I want to learn to build a real LLM so I looked at https://allenai.org/olmo where there are instructions and ingredients. But, unfortunately I can't afford the required compute resource so I will have to wait for a bit I guess.
Anyone got any hints or tips for me?