[rg111]

My suggestion would be to learn all the stuff from this course, using fast.ai library, and then gradually move towards PyTorch.

fast.ai is a fantastic educational resource and a great way to approach solving problems. But the library itself is lacking, and if you are an experienced programmer, when building real-life projects, you will be frustrated with fast.ai library.

The goal, IMO, should be learn from Jeremy Howard, s great instructor, communicator; learn his attitude, and then move to PyTorch (keeping the attitude, the knowledge, and the lessons with you.)

[mloncode]

I am an experienced ML Engineer of 10 years and have worked at several large flagship tech companies. I do not agree that fastai is not appropriate for real-life projects. If you know the fastai library well, you know its a layered api on top of pytorch, which allows you to customize things to your needs quite easily. For example, it is fairly straightforward to get any pytorch model out of a Learner object. Furthermore, lots of care has been taken to keep the apis very consistent with pytorch as well.

It's also the only library I know of that consistently bakes in best practices like super convergence techniques or making things like test time augmentation very seamless. Many libraries lag behind fastai 1-2 years in this regards, and frankly it can be frustrating to use other frameworks sometimes.

There is a slight learning curve, for example to learn the DataBlocks API or the callback system, but once you really understand what is happening you will understand how nice the API is and how well engineered it is.

Side note: Regarding being an experienced software engineer, I highly recommend digging into how the python language was extended for this project (fastcore) and the development workflow used (nbdev), which I think could be interesting for those software engineers you mention as well as heighten your understanding of the ecosystem of tools.