bkcooper

Beyond that, I think maybe I would find some specific codebases that you'd like to understand better, and start with reading more of those. I feel like that's often better than books for picking up idiomatic usage and patterns in given domains. As you hit specific barriers, I think it will be much easier to pick up the intrinsic motivation to dip back into theoretical knowledge at that point.

Comment · 2 years ago

Hadean Lands is also very cool

Comment · 2 years ago

Yeah, I would not point somebody unfamiliar with Stern-Gerlach trying to learn "the basic idea of quantum mechanics" at Sakurai. Feynman lectures vol. 3 maybe

Comment · 2 years ago

Well, the characters are stuck on a primitive planet in the Slow Zone so if you go in expecting Space Opera then you’ll be disappointed.

Except half of Fire Upon the Deep was characters on the same planet but it was actually cool. The first two books are definitely among my favorite sci-fi of all time, the third one was a dud.

My main gripe is that these three books all share the same trope that underpins one of the major subplots: glib, charming politician type is scheming, eeeeevil. In the first two books, there's enough novelty (how the Tines and Spiders work, programming as archaeology, localizer mania) to make up for that. But I don't really think the third book adds much in the same way, and it is also very clearly building to a confrontation that will happen in a future book. So the staleness is much more noticeable

Comment · 2 years ago

I spent quite a bit of time at the end of high school into early college playing on a Forgotten Realms themed MUD. It accomplished two valuable things: it durably increased my typing speed by 20-30 wpm, and it also inoculated me against the MMOs which would have been way more destructive for me.

Comment · 3 years ago

You might be interested in Kip Thorne (of Gravitation fame) and Roger Blandford's book Modern Classical Physics, which is designed to cover the elements of non-quantum physics that are generally ignored in the first year PhD curriculum. Part headers: statistical physics; optics; elasticity; fluid dynamics; plasma physics; general relativity

Comment · 4 years ago

I largely agree with your comments in this thread -- I'd been thinking about trying to express the same thing myself. I'd been guessing the motivation would be ML, where I feel that most people substantially overestimate how much they'd need.

Signals processing, though, is one of the places where I actually think a decent understanding of some of the higher-level concepts in linear algebra is really helpful. Linearity itself comes to mind, and maybe it just reflects my physicist's education, but it's hard for me to imagine having a working understanding of Fourier transforms without getting the idea of changing bases. I feel like you're about halfway through a first linear algebra course before you'd get there.

EDIT: that said, a good signals processing book probably covers a lot of this in sufficient depth if you can figure it out. The other catch-all comment I'd make is that linear algebra from a math class can look somewhat different from practical linear algebra on a computer. (That it's often a bad idea to directly invert a matrix for many computing applications is non-obvious from math class.) A book like Trefethen and Bau is great on that latter subject but is not a good starting point for OP.

Comment · 4 years ago

When the conductor is grounded, the charge on the conductor is no longer constant. It can change until the total enclosed charge in any sphere surrounding the conductor is zero.

Comment · 5 years ago

Nocedal and Wright is good. +1 also to the suggestions for Boyd and Vandenberghe. I really like Boyd's writing in general; he has coauthored some good review articles on proximal algorithms and ADMM.

A couple of other suggestions:

Nesterov's Introductory Lectures on Convex Optimization. This one is pretty tough sledding, but I found the perspectives in the first chapter particularly to be enlightening. It seems like there's a newer Springer book which is probably an expansion on this.

Bertsekas's Nonlinear Programming. Bertsekas has written a lot of books, and there's a fair amount of overlapping going on. This one seemed to be the one that has the most nuts and bolts about the basics of optimization.

EDIT: If you want more understanding of convexity beyond what's presented in these books, Rockafellar's Convex Analysis is helpful.