
Deep Learning Algorithms: The Complete Guide - sergioskar
https://theaisummer.com/Deep-Learning-Algorithms/
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rckoepke
Mainly I take issue with the title "...: The Complete Guide". More accurately,
its a very abbreviated overview. The table of contents has a fairly reasonable
list of topics, but for a complete guide I'd want each section there to be at
least an entire chapter of material. Instead they are 4-6 sentences per
section.

Personally, I think that any comprehensive guide/textbook for machine
learning, especially deep learning, should contain a chapter on the
mathematics of control systems theory. The feedback/back-propagation is very
similar to what EE's and ChemE's do with PID loops or Op-Amp tuning, and I
feel that so much is lost academically when that topic is glossed over.

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throwqwerty
>The feedback/back-propagation is very similar

i really wish people wouldn't say these things just to sound smart. it
confuses people that don't know better. feedback loops have nothing to do with
backprop, which is just a way to factorize the jacobian into matrix vector
products instead of matrix matrix products. do you have some proof (and it
would require a proof) that PID controllers actually compute the gradient of a
function with respect to n-inputs? because that's what backprop does.

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rckoepke
Thank you, non-sarcastically. Would edit if I could.

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throwqwerty
i've said it before and i'll say it again: this is content marketing (for some
kind of bs courses or something). why are you people upvoting this

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Hydraulix989
No deep reinforcement learning?

