> My problem with systemd isn't pid 1. It's all the rest of the shovelware that comes along for the ride. And it's the "you'll use what we tell you to use" attitude of the project.
The problem with this in practice is that you're often not the person implementing the 3rd instance. It's some other dev who comes and sees 2 copy/pasted implementations, either figures there's a good reason for it, or is lazy, or is risk adverse, or just wants to get the job done and move on, and copy/pastes it a 3rd time.
I wrote the python compiler. It's very slow. With the optimizations it's probably something on the order of O(n^3) or O(n^4). One of the test cases took seconds to compile. I made no effort to optimize the compiler speed.
There is a mandatory compilers-lite course at UW and he teaches the advanced version of it, where the project is to fill in pieces of a Scala-lite compiler written in Scala.
Hi. I wrote the python implementation. I've had prior experience with compilers and I'm obviously biased but I would say it's quite readable. The main compiler part (without optional features or the LR(1) or lexer generator) is 2040 lines and contains very little magic. It would be easy to translate 1-1 to idiomatic C++ for instance - it would just be much more verbose. eval() is only used by the lexer generator. The metaprogramming could have been avoided by using dataclasses (but the marker used python 3.5).
(To be clear, the optional portions in which I implement SSA, various optimizations, a Hack-style allocator and some ad-hoc codegen is much less readable.)
I'm guessing this is because most people who trade tether don't intend on holding it for long, so the "shennanigans" surrounding tether aren't as likely to affect them.
I don't know much about AI but for ML specifically Elements of Statistical Learning is fantastic. I find its explanations a lot easier to understand than other resources. I recommend you skim through it to get a taste. Additionally if you prefer lectures ETHZ has recordings of their ML class[1].
The best way to learn the details is of course to read the original papers. This is especially true for following along with the latest developments in deep learning.
This is indeed an interesting problem. If you just want to minimize the number of rectangles there is a neat solution on stackoverflow [1] that uses minimum vertex cover (equivalently, bipartite maximum matching).