Do problems involving the equations on paper and also read and copy down articles that are written on them.

I think you need to research the basics of ai hardware. Laptops are almost all worthless, with the exceptions being specially designed laptops, which still only provide mediocre performance.

There's no such thing as future proof in ai. Focus on present proofing

I was able to run the 7B on a v100 on lambda labs. Didnt try the other ones.

You just use mfcc and then it’s just like image detection

Ai is inherently filled with theory and concepts. Not knowing these concepts will prevent you from building much of anything useful currently

Start with supervised learning. Then move to deep RL with cart pole or. something. Thennnnn Dota 2

Write all the equations out one paper, then do one forward and backward pass on paper as well with a simple mlp. I believe bias can be easily incorporated using an extra 1 in the input and using an extra weight as the bias, so it’s updated the same as any other weight. Also learn the basics of matrix multiplication.

Deep learning is much different from typical programming topics in that it is composed of a large amount of mathematical and complex theoretical concepts that are not avoidable using a library. Getting the code to run is relatively easy, and the choice of library has mostly to do with deployment goals and utilizing existing implementations. When things aren't working, theres not really a compiler error as to what's wrong with the model/data pipeline, and deep theoretical knowledge comes into play.

Hands on machine learning with tensorflow and scikit learn

learn about how neural networks work.

The confusion matrix looks fine. Your model is just predicting the same class for every input

1080ti

oh, i just was thinking it could be useful (possibly making one myself) as I feel like this is a common issue for people.

thanks for the thoughtful feedback! Also lmk if you have any feedback on the pip package idea above that I added to the post!

Don’t overthink it. It takes a long time, so just do whatever interests you

Ok, thanks I think that clears up the drawbacks. I’d have to check which motherboard I’m using now, but generally would you expect a 3090 to be compatible with the motherboard that works with a 1080ti? Thanks

I believe 2 k80s = 1 1080ti.

Will look into that.

So I did some research. According to the lambda labs website, 4 1080s combined will get me 1.5x throughout as a 3090 with FP32 training. FP16 seems to yield a 1.5x speed up for the 3090 for training. So even with mixed precision, it comes out to be the same. The actual configuration of 4 cards is not something I’m very familiar with, but I wanted to point this out as it seems like NVIDIA has really bullshitted a lot with their marketing. A lot of the numbers they throw around just don’t translate to ML.