DL has not just arrived. It has been going on since before 2010 in industry and universities. There are multiple cohorts of PhDs in it. DL is useful obviously.

DL has not just arrived. It has been going on since before 2010 in industry and universities. There are multiple cohorts of PhDs in it. DL is useful obviously. Some of the biggest products in the world depend on it for almost their entire value propositions e.g. YouTube, Netflix, Tesla. (This is a power law; the long tail of commercial AI are jokes or borderline frauds.) But I wouldn't recommend already practicing programmers to FOMO into the field this late in the game. First of all, it's very much a different animal. ML is in the statistics/continuous world whereas programming is in the algorithmic/discrete world. Knuth said something about how it's a different type of mind (a more normal one) that's attracted to ML, not the same as the 2% computer mind. Not only will you be doing something you're potentially not as talented at, you will also have to start from scratch up against the seemingly endless stream of ML specialist PhD candidates. You can implement neural nets from descriptions in papers and blog posts. Honestly though there's not much to it with all the dead simple libraries e.g. Keras, Pytorch. Putting together the whole system is a much harder problem and requires actual software expertise.