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There are already a few university ML courses out there using Theano (for which Tensorflow is essentially a drop-in replacement), and I think this will be a much bigger trend over the next few years. IMHO for a first course it's useful to do some work at the Matlab/numpy level just so you get experience with deriving/implementing gradients yourself, but for larger (deep) models automatic differentiation is an amazing productivity boost that should make it possible to cover a lot of interesting topics that you'd otherwise not have space for.



actually numpy would be a brilliant starting point - but I'm not able to find any popular ones that dont use matlab (or some dialect thereof).

disclaimer: I have no idea what I'm talking about, but I do know that coursera and stanford courses are the oft cited ones and they use matlab/octave.


The same goes for Torch: New York, Oxford ...




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