5olidor

It seems like reinforcement learning would be useful, i.e. at a high level, forming a policy for recommendations would require balancing exploration (experimenting with more risky recommendations) vs. exploitation (showing you recommendations that it knows will likely lead to clicks) and using the click-throughs, time spent watching the video, etc. as reward signals.

Does anyone know whether RL is used for recommendation in practical settings, and if so what is the current state of the art?

Apostol's calculus texts were my first experience with 'real' proof-based mathematics, and I also learned real analysis through his Mathematical Analysis book. Good times; his books taught me a lot.

He wrote with clarity and conciseness; when I read Apostol it always seemed like not a single word was added unnecessarily. I highly suggest picking up his books, and am sad to see that he passed away.

On the probability side, I thought Ash's "Probability & Measure Theory" was good for self-study, although some experience with real analysis is definitely a prerequisite. It can be pretty dense at times, but well worth going through.

I like the observation about the importance of concentration.

In addition to reading, I've noticed that studying mathematics is an opportunity to practice (or build) the ability to focus on frustratingly difficult material that requires a high attention to detail.

That makes sense, thanks

Agreed, I was just curious since some of the comments below seemed to imply that PhDs / PostDocs lack certain software development skills (although I personally haven't seen this). If so, this would either suggest that the code quality in computer company labs may be low, or they hire based on research potential AND ability to develop software

Does this also apply to large industrial labs, e.g. MSR, IBM Research, Google research etc.? Or are there software developers who work together with researchers in those places, perhaps resulting in better quality code?