Fast.AI is the best course out there if you had like to get in to applied side of deep learning. However, I wish people would be more careful about their claims

Fast.AI is the best course out there if you had like to get in to applied side of deep learning. However, I wish people would be more careful about their claims. It's one thing to mechanically apply same technique to same problems. It's quite another thing to compose known techniques in a new way to solve new problems. And still quite different to invent brand new techniques to solve previously unsolvable problems. The first category is "practitioner", second is "engineer" and the third is "scientist". To transition from practitioner to engineer, one will need to know low level details including directly relevant math and popular prior art. One of the distinguishing difference between a practitioner and an engineer is that later needs to be able do systematic debugging to identify and fix the problem. I often ask folks during interviews how they would go about when their ML model to do X doesn't work as expected. This usually reveals the difference between practitioner and engineer with high accuracy. To transition from engineer to scientist, one needs to know much broader body of prior art and beyond directly related math. A typical interview that would differentiate between engineer and researcher would be to discuss some known unsolved problem and see if the person can elaborate details of prior art, opinions of top researchers, wins achieved so far and directions for future efforts. This is again not to say that practitioners can't do debugging OR that engineers can't make ground breaking innovations. Faraday, Edison, Wright Brothers - all either started out as practitioners or engineers and successfully attacked some of the hardest unsolved problems of their time, never obtaining formal academic credentials. On the other end far many more academics have broken new grounds then non-academics. At the end of the day its all probabilities. Claiming that PhD is no longer required to do "AI" doesn't reveal all these subtlety and marginalizes tools and foundation that PhD equips oneself on this journey.