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Oh, neat! I went through an earlier version of the online course when I was just trying to understand what this "deep learning mumbo jumbo" was all about, and it was the clearest, and easiest to follow, and most interesting one available, by a long shot! One of the assignments had you train an image recognization model based on google image results, and after a shockingly small amount of work and time I had a model that could distinguish a picture of a game of Go from a game of Chess almost perfectly. That was a huge eye-opener for me.

That was maybe 1-2 years ago at this point and I had wanted to take another look. What a perfect opportunity! And I'm excited it sounds like there might be a little more discussion of non-DL ML and applications in tabular data (where I'd have the most likely use for it), as well as the nitty gritty like deployments and use in production!

Any progress on the Swift front? Is that mentioned / used / discussed at all in this new course?




Sylvain was working on fastai for Swift, but he became busy with the book and course and also has left for HuggingFace. Jeremy has not been working on Swift. No lectures on Swift in the course. The Swift4TF team is still active, though Chris Lattner has unfortunately left.

Another project that is similar is Fastai.jl, a port of fastai to the Julia language. It is still in active development: https://github.com/FluxML/FastAI.jl


It doesn't appear that the Swift port for fast.ai is a high priority, but Jeremy, Sylvain, and co have rewritten the Python library from scratch and it is much more sensibly organized now. The version 2 codebase is completed developed (generated, along with documentation) from Jupyter notebooks.




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