ade17_in

joined 10 months ago
[–] ade17_in@alien.top 1 points 10 months ago (1 children)

100%. I did exactly the way you described and it works awesome. And got to know it's called "persistence".

I implemented a very interesting and creative approach to train such a model, and will surely make a post here later for a detailed summary.

 

These are plots of some of my features and rest of the others having similar pattern. The data here is spanned for 2 days and I need to predict labels for 3rd day. It has 60,000 samples (seconds).

Any popular time series regression methods or repos I must be aware of to solve this kind of problems? I don't need to forecast as I have labels for validation.

Also what are current trends for statistical models vs ML models.

Does considering lag or sliding window the only popular and effective option?

https://preview.redd.it/9vjh75m7ki2c1.png?width=1354&format=png&auto=webp&s=f1da6b7fcda0cd96f4b997b818b0272f911cf120

 

LLMs are trained on reddit/quora corpuse as well (correct me if wrong). So do the 'number of upvotes' on a reply/answer was considered as a parameter or feature during the training?

Also it's not just about just reddit/quora but is answer realibility factor which is most case are its upvotes are considered?

Or being a "language model" it does evaluate and find similarity itself and chose what to retrive, might be the reason we see hallucinations.

[–] ade17_in@alien.top 1 points 10 months ago

Thanks! Bombed that interview but will try it for my next one!

[–] ade17_in@alien.top 1 points 10 months ago

Thanks for this. Super useful

[–] ade17_in@alien.top 1 points 10 months ago

Thanks alot. Surely helpful

 

Hello folks,

I have an interview call later this week which the work is regarding implementing generative AI within the companies workflow. Using LLMs with finetuning/in-context learning using system logs etc kind of stuff.

I have studied machine learning, worked for few years now as well. Have good understanding of those stuff but never tried fine tuning hands-on. I'm worked majority into computer-vision applications but think that I lagged a bit on the LLM side.

Any suggestions, recommeded papers, courses, videos I could go through?

Thanks!