Mostly great information, but it seems like he was speaking about 4 year old TensorFlow and Keras about 2 years ago.  We use 12 different frameworks, 4 on a regular basis.  Many of the supporting libraries and extensions are able to be used across most of the frameworks in fairly equivalent form.  The appropriate development frameworks and supporting libraries really depends on the specific problem space, requirements of the delivered solutions and other factors.  Swift and various ancillary language environments, including Node.js (used correctly), enables amazing productivity and performance.  I totally do NOT agree with starting on Fast and PyTorch, as it is like when they taught Pascal because it was a better learning language with fast ramp-up, even though it was built with a limited professional carry-forward in mind.  The basis of PyTorch is somewhat self-limiting, and even Jeremy asserts the same in this and many videos.  <br><br>I have generally found that the best tool at the time is the one you know best that can solve the given problem.  If that is the case, why not learn the most robust long-term viable solution set first, and become familiar with secondary environments like PyTorch along the way?  That way you get the best possible long-term reusability of skills and are buying into an architecture that is not edging towards marginalization or end-of-life.  You can and many should learn Py-based frameworks along the way, while they are building a development skill set that they can grow with for many years.