It's pretty easy when you're talking to people who understand the fundamentals of deep learning, but that understanding isn't very common even on HN. I think th

It's pretty easy when you're talking to people who understand the fundamentals of deep learning, but that understanding isn't very common even on HN. I think that's because the real-world, valuable usecases of DL are not very accessible: (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.