Twice a day is like standard minimum

OP definitely needs to change it more often.

They aren’t requirements, just some paths worth going down.

EE to ML guy here. Copying some advice I sent someone else. It’s not exactly what you asked, but might be helpful.

Spend some time at a low level: use only Numpy to write a NN including backdrop. It’s feasible to do this for reasonably complex nets. For example the DRAW network by Alex Graves. You want to read the first 1/4 of David MacKay’s book. Watch the NYU DL video series and try the notebooks. Do the same the UvA I think that reading lots of new papers is a bad idea, especially as people create elaborate explanations for things. Instead, find some people to follow on GitHub who are coding with the latest ideas. Having said that, there are quite a few ppl who you want to read/listen their every word (Yann LeCun comes to mind). Listen to Andrej Karpathy to see what DL looks like in production. Don’t shy away from working on core basics like linear algebra.

Embedding are dense layers, just for one-hot vector input.