Also the number of DL submissions on HN seems surprisingly low given the applicability of the technology.
(a) DL is pretty complicated in a way that's unfamiliar to most software engineers. You are consistently working with Tensors that have a couple more dimensions than people are used to holding in their heads (i.e. images mean you are typically working with 4D Tensors).
(b) You learn from academic papers, not blogs. It's a new workflow for many software people and intimidating to some (although the papers are usually closer to blog posts than rigorous academic papers).
(c) It's very difficult to learn deep learning on your own without it getting pretty expensive. Advanced uses pretty much require GPUs/TPUs and that's either a big upfront purchase or a serious per-experiment cost.
(d) Deep Learning is not a single field. It is CV, NLP, RL, speech recognition and probably others I'm forgetting about. They overlap, but it further reduces the number of people you can have informed discussions with because being knowledgeable about computer vision does not mean you are able to have a vibrant discussion about NLP.
Any recommended resources?
These are “hands on” in the sense that you can replicate the results just by pasting in the same code. It’s kind of like a tutorial notebook in essay form.
Speaking of tutorial notebooks, pbaylies’ stylegan-encoder is quite good and you can run it on colab: https://colab.research.google.com/github/pbaylies/stylegan-e...
(Set runtime to GPU up in the menu.)
https://github.com/pbaylies/stylegan-encoder
In my experience the best place to have informal ai discussions is Twitter. The community is shockingly helpful. Follow @jonathanfly, @roadrunning01, @pbaylies and whoever pops up in the stuff they post. Roadrunning in particular posts tweets of the form “here’s some research; here’s the code” often with an interactive notebook.