I wonder if students that take this class are better at throwing their pile of data into Tensorflow.
To a software engineer accustomed to operating on layers of abstraction far removed from the hardware, this may seem a reasonable point. Why is it worth learning that pesky math, anyway?
I would argue that the machine learning engineers of today are more like electrical engineers than programmers, however. When something goes wrong, you don't have nice warning messages or error catching available to you. Like an electrical engineer with a voltmeter, one must begin probing inputs and outputs each step of the way. Good luck doing that if you do not understand how the components are supposed to work.
YMMV by copying and tweaking others code, but I believe we are still far off from hands free 'autoML'. Just ask anyone who has sent a model to deployment whether AWS autoML was sufficient for them. And whether they needed someone who understands backprop at some point during the model training process.
It had never ever worked for anything practical.
And no, Deep Learning is high science. It is not just throwing piles of data to PyTorch and run it on a 8-GPU cluster.
I don't blame anyone having this view. Megacorps with billions of bucks are in a race to train the biggest language models, and that is the news every media focuses on.
But we will, someday.
We, once considered the existence of "unknown forces" in basic planetary motion. But we figured out later.
There are many directions, one of them being the work being done by Bronstein et al. w/ Geometric Deep Learning.