For people who know the basics this article describes Pytorch in more detail: http://blog.ezyang.com/2019/05/pytorch-internals/

One Quarter of the way through this playlist now. It's very good!

I'm having to learn this framework for a course assignment, and I feel a lot better about it now than I did after going through the OP.

Thanks for sharing!

Do you have any more recommendations?

I'm an undergrad student, and I'm nervous about picking between Tensorflow+Keras over PyTorch.

It looks like many more companies are hiring for TensorFlow, and there's a wealth of information out there on learning ML with it. In addition, it just got the 2.0 update.

But, PyTorch is preferred nearly every single time when I see the discussion come up on HN and Google searches. I'm having a hard time deciding what to dedicate my time to.

Abstract from the tools. They come and go. You will need to adopt a new one every other year.

Instead, make sure to understand the math and the concepts, and then it‘s easy to translate that to an implementation.

One way of doing this (though not sufficient) is to learn both tools.

Right now the pull is away from TF (increasingly convoluted API and lots of deprecations) and towards pytorch (more support from the research community and increasing performance in production).

I'll recommend fastai course [1].

At the end of the course you will be able to implement almost any ML state of the art solution (classification, regression and Computer vision).

Sounds too good to be true? Jeremy have that effect, The other day in a podcast they told him Saint.

[1]: https://course.fast.ai/

It's free btw.

My 2c: learn the methods deeply, pick up the frameworks as needed. Knowing PyTorch or TF well won't make you a good data scientist or statistician.

YMMV