Hopefully this is no against any group rules, but I’m a DS master degree student coming from a CS bachelor, and I really love DeepLearning and all the magics that we can do solving optimization problems, even without NN involved.
I have a good preparation from the theoretical POV thanks to the university, and i’ve coded manually many optimization problem calculating gradient by hand, however I love the idea of autodiff that TF and PyTorch gives out of the box, and I’m really looking forward to learn TF from the ground up, however I really struggle to find material that does not lead in just stacking layers on a sequential model from Keras…
My aim is to be able to take an idea of (example) a layer, and code it using tensors and autodiff from TF, and not looking for online code that already solves that (or even maybe optimizers, since I’m pretty familiar to many other not already implemented in TF)
Do you have any online resource or book that you feel that is a good starting point? I usually learn hand on and reading Docs, however I feel like TF is better to learn it how it’s supposed to, to fully grasp everything that it can offers
In other words, I have a good theoretical preparation on ML/DL but I feel I’m lacking in a more practical aspect… so… how/where can I learn to use GradientTape and of those magic things (everything is accepted, online offline, paper digital, paid not paid)?