Fastest way to relearn machine/deep learning I hope I came to the right place to ask this question.

Back when I was at collage I studied machine and deep learning in-depth. My whole programme was based on those areas. I knew all underlying maths, even today I know how to derive backpropagation for any feed-forward network. Well, maybe I would need to take a peek. But I still understand the math and I can follow without problems. Back then (2017) I was even doing something with and researching GANs which were novelty back then. 

Basically, I was at the hotspot at that time. I was working with all sorts of algorithms, from logistic regression, SVMs, to MLPs, CNNs, RNNs (mostly LSTM) and was trying already mentioned GANs. Oh, heuristics too: GAs, tabu search, simulated annealing, etc. There was also some NLP involved too.

And then after college I went to gaming industry, heh. I was/am still, during that time, working with ML/DL (and some OpenCV) but mostly easy, toy projects (although one project was real life project, but it was easy, I had to extract written digits from paper and classify them).

So, my question is, what is the best (and fastest) way to get back on the track considering my, I would say, pretty strong background? I saw that Kaggle has some courses on their site, for example course on DL is estimated 4 hours, course on feature engineering is also 4 hours. That is not a lot of time, but I am afraid it would be too easy for me and consequently a waste of time.

What are some good resources to refresh/relearn ML/DL considering my background?
If you enjoy reading, that's probably the best ML/DL book ever.