YOLOBOT666

YOLOBOT666 t1_j7wrm1z wrote

What about saving the dataset into batches as individual files, then use the data loader to load the files as batches for transformers? Keeping the batch size reasonable for the GPU memory.

For any preprocessing/scaling, this could be done on the CPU side and would not consume much memory^

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YOLOBOT666 t1_j7iov1k wrote

Nice! I guess the heuristic part is how you use the queries at every iteration and make it “usable” in your iterative approach. What’s the size and dimension of your dataset? These graph-based ANNs are memory intensive, wondering what can you do for your dimensions?

If it’s a public repo/planning to release it on GitHub, I’d be happy to join!

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YOLOBOT666 t1_j6ziz4m wrote

Yeah, this would be a course in RL, most likely using RL bible as main reference textbook. Agree with the other comment, these lectures are all available online.

What I found valuable in attending a course in person was the prof, lots of insights and intuitions explained in person/office hours was the most valuable part for me. While I was taking the RL course in person, I also referenced online lectures and notes.

In terms of data science interviews and jobs, Bayesian would be more useful, at least more than RL unless you found yourself in robotics or some very niche industry.

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