I say "knowing" because I’m no longer sure of what the word "learn" means exactly, epistermologically. I’ve been studing data science for 2 semesters. It seems the big data hyme is over and everybody is talking about deep learning, AI, etc. now, but as a matter of fact, neural network IS very powerful in solving certain problems, though in a mysterious way. It will change the landscape of many sections in the ecomonic sections for sure. As a cs student or professional, having some deep learning literacy may be rather desirable. Most people are unable to invent new algorithms or models, even with a PhD, but only knowing how to use libs and tune params is not enough. We may need a broad, not too detailed, but working understanding. Here’s a roadmap to acquire that kind of understanding. First and foremost, some grasp of theory is a must. The book by Ian Goodfellow et al should suffice. If you’ve been off campus for long, recap of some rudimentary statitics may also be necessary.
Then get your hands dirty. Implement some naive NN, a MLP, a vanilla CNN or RNN without using libs. Some keys of designing a model like dataflow exchange, forward and backword and so on may be worth your additional attention.
Finally you need to work with libs. After all, that’s what the vast majority of AI/deep learnring/machine learning engineers do. There are many. Pytorch and tensorflow in order is qutie orthodox. The learning curve beginning with pytorch is steady. There are many tutorials online you can follow.