I still think the software stack for probabilistic programming has a ways to go before it becomes as easy to use as a NN using PyTorch, but it should get there in the near future. I’m personally very very excited about the probabilistic programming approach — conceptually it’s a very smooth segue from structured numerical algorithms, and allows you to really exploit problem structure if you have good domain understanding.
For me, it helps organize a lot of well-known algorithms as special cases of a general framework—which is worthwhile in itself. If I can code in the generic framework, and have the compiler generate the appropriate (optimized) special case algorithm (as one hopes), that’s icing on the cake.
[0] https://www.youtube.com/watch?v=zKUFSKRjTIo and also https://github.com/dotnet/infer
Helps gain very nice and concrete intuition, before getting lost in math or code.
Which well known algorithms do you have in mind here?
Difficult to give a quick answer. I’m also not aware of any good resources where this is spelled out. If you’re seriously interested, feel free to hit me up for a deeper discussion.
My brain wants this term to mean something else and I become momentarily excited every time this topic gets reposted.
I’m unrelated to the author; just came across the book on a Reddit discussion and found it interesting. There aren’t too many (collected) discussions of these kinds of topics, AFAIK.
with thorough documentation:
I recently started going through it again and it's pretty fascinating as someone not familiar with the field.
