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cma_4204 t1_j43deyz wrote

All you need to know is git clone

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derpderp3200 t1_j45p9o7 wrote

One day somebody will implement git glue that stitches code with LLMs and you'll never type any other command again.

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currentscurrents t1_j44pu0u wrote

Is it though? These days it seems like even a lot of research papers are just "we stuck together a bunch of pytorch components like lego blocks" or "we fed a transformer model a bunch of data".

Math is important if you want to invent new kinds of neural networks, but for end users it doesn't seem very important.

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EmployeeOne817 t1_j45v4j9 wrote

Hah sticking things together will only get you so far. True innovation and improvement of existing solution comes from fundamental understanding of these theoretical concepts.

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UpperCut95 t1_j46b1rt wrote

Totally UNDERRATED.

The whole research industry is chasing the x% performance gain while the train/compute/energy cost increase by 10x%

Aiming for efficiency and interpretability would be a good way.

But meh.

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derpderp3200 t1_j45pioz wrote

I imagine it's important when you're theorycrafting about whether a novel architecture will be able to propagate gradients in a way that might facilitate learning things, but yeah for the most part it seems about intuition and copying successful approaches more than anything.

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neotod_ t1_j43f0lr wrote

I started also this journey in about 2 months ago.

Was looking for some peer on learning this beautiful beasty.

I think this kind of learning is more effective than solo learning. This way we define some problems and try to find solutions and ideas for them each other. That's very better I would say than doing these things alone.

I'm currently learning based on Hands On ML book chapters.

Wanna join me?

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