$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 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.
As with any powerful but potentially-hazardous tool that is not perfectly reliable, I personally prefer to know what I'm doing and how the tool works. That's why I still have 10 fingers, 2 eyes, and a job.
And Claude or gpt can make websites just fine. So can the open weight models.
Anyone got any hints or tips for me?
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This is somewhat true, but really the motive is providing a soverign instance that cannot be withdrawn for arbitary reasons.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.