A Brief Introduction to Machine Learning for Engineers (2017)
This makes a good case for not learning machine learning three years ago. If you wait long enough for new paradigms to coagulate, you can use your energy more wisely during the chaotic transient period (e.g. by spending your effort on time-invariant parts of human knowledge, such as gaining a deeper understanding of parts of pure mathematics, or even philosophy or history), while you wait for a new set of fundamentals to converge, and really solid pedagogical explanations to surface.<p>The same could be said about skipping the earlier period of javascript frameworks. Had you used your time to learn things that are still useful today, and will remain useful decades in the future, while waiting for the industry to jump here and there, eventually converging to what seems to be a fairly robust steady state (react), then you'd probably be better off today than if you followed along with the hype cycle month after month.<p>A counter argument is that you might miss out on a lot of the monetary rewards that comes from learning the much desired skill that others don't have the time or will power to tackle in the period where doing so involves a lot of friction.