selfire1

2022

Daily Journaling

const dayInMonth = moment().format("D");
const currentMonth = moment().format("M");
var i = 1;
while (currentMonth > i) {
    var entries = entriesSum(i);
    var max = daysInMonth(i);
    var perc = entries + "/" + max;

leizaf

const std = @import("std");
const cc = std.ascii.control_code;
const startsWith = std.mem.startsWith;

fn isDigitNotZero(c: u8) bool {
    return switch (c) {
        '1'...'9' => true,
        else => false,
    };
}

yoavg

Learning LLMs in 2025

So you know how the transformer works, and you know basic ML/DL, and you want to learn more about LLMs. One way to go is looking into the various "algorithmic" stuff (optimization algorithms, RL, DPO, etc). Lot's of materials on that. But the interesting stuff is (in my opinion at least) not there.

This is an attempt to collect a list of academic (or academic-like) materials that explore LLMs from other directions, and focus on the non-ML-algorithmic aspects.

Courses

• David Chiang's Theory of Neural Networks course.
• This is not primarily LLMs, but does have substantial section on Transformers. Formal/Theory. More of a book than a course.