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As a complete non-expert in ML I have watched some of the fast.ai videos and dabbled around with their library, and generally I like very much their approach of trhing to make state of the art ML as easily approachable as possible for practical purposes.

However, what worries me a lot is complete breakup of the API between versions and some discussion about Swift. As a non full time expert I would need to have a robust and stable framework to keep on learning so that I can keep on building knowledge without needing to be worried that whatever I have learned about how to use some framework (or which language to use!) Will be obsolete in a couple of months and I need to start from scratch again.

So, does anyone know if fast.ai is going to stabilize anytime soon, or is it better for me to just try to spend the little time I have to play with ML and deep leaening directly with e.g. pytorch?

It's a fast moving field. In the past 5 years we've scene enthusiasm for Caffe, luatorch, theano, tensorflow, and most recently pytorch. And tensorflow 2 of course, which totally changes the programming model. Our own library changes to keep up to date with the field.

Having said that, v1 isn't changing much now - v2 (out soon) will be where new ideas go, and only bug fixes will go to v1, so you can stick with that as long as you like.

> It's a fast moving field.

Obviously it is moving fast at the bleeding edge. But the basics of backprop have not changed for a while, and somehow I would think that a toolchain that I would expect to be a serious tool for practical purposes and not just a display of the bleeding edge would at least be backwards compatible with previous versions.

(I hope I am not writing this in too harsh way. I actually liked your videos so much that I am seriously considering applying for your on site course if I one day just get all the other issues sorted out in my life so that I can relocate to SF for a couple of months)

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