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The lectures have a ton of gems in them, but I have a hard time following the lectures as a beginner/intermediate practitioner, as around 1-5 percent is relevant for me. I don't particularly like the fastai library as it feels like an obstacle between me and the underlying pytorch library. After trying to use fastai for some time, I started looking directly at pytorch and found that all the abstractions and features I liked with fastai were actually from pytorch. That said, it's a great resource and if you're doing cookiecutter stuff it seems pretty nice. Also, students of fastai have done some pretty amazing things. I like how Jeremy emphasizes the top-down view, but it's very hard for me to submit to it. I can't shake the need to understand the underlying ideas and build stuff bottom-up, even though I see my progress suffers from it.

The second part of the course (https://course.fast.ai/part2) - builds stuff bottom-up, starting from matrix multiplication all the way up to ResNets. This is a great resource even if you want only use Pytorch.

Great tip. Thanks:)

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