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Usual reminder: when I've been saying for the past 5+ years that deep learning is interpolative, I don't mean it does linear interpolation in the original encoding space (which would be useless). It does interpolation on a low-dimensional manifold embedded in the encoding space.
Anyway, check out chapter 5 of my book for details on how DL models manage to do it, and why this is conducive to (local) generalizationhttps://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff …
Manifold interpolation is like asking: what's the mid-point between a fork and a spoon (it terms of their function)? Which you can approximate if you've got a database of utensils related by pairwise functional distance
Taking a bunch of numerical features (e.g. images) describing spoons and forks and interpolating linearly between them would be entirely meaningless...
If you want to be very good at ML do you think python is still the best way to go. Sometimes I wonder if python is limited 
. I learned the language but I struggle to love it.
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