The Fast Track – Sheafification
"textbooks one can study to reach expertise in mathematics and physics in the most efficient manner possible"
"textbooks one can study to reach expertise in mathematics and physics in the most efficient manner possible"
postgres db in the browser. Requires log in for the AI bit
Great list. Learning things, automation, and coding one-offs
Programming resources someone has built up over years. Like a "getting started" guide but they kept adding to it
not sure if I would actually find this useful, dense multi-arrowed charts are often hard for me to follow. Cool though!
when more familar with a topic, we use more jargon and write less directly
Graduate-level introduction to graph theory, for Math 530 in Spring 2022 at Drexel University
Don't stop at the first sentence like I did on first open.
Unless you want the opposite takeaway
great write up of working through some prompts and backgrounds used for some drawings. For some reason I particularly liked how they added birds
Some minigames and exercises to practice touchtyping
My suggestions:
Thread features some good discussion on the "I've been doing it for decades and still learning" as encouraging or not.
Recommendations:
Found via lightnote
drilling at 1000 most common words. Quite addictive
this is awesome. Dissecting and assembling different types of beats
Thanks Alexandria \U0001F49C
Recommended by some random HN commenter
Two key parts: start small and play slow!
Treating practice different to performance. Until you can actually play a piece just do drills, then gradually build up to larger sections. But do the drills slowly and correctly, rather than trying to play at actual speed with bad form.
Some good resources for free sheet music and other resources for learning music. On the actual site I saw the 'have Amazon print it for $9.53 paperback bound book', which is an interesting concept.
I actually started using Anki a little after this (installed it and set up some decks at least). Haven't stuck with it yet but we'll see
Love this term - this is exactly how I learn. Start by skipping over the details,
develop a coarse understanding, then refine your knowledge by learning about specific areas in more detail.
Interactive examples to learn through reading and play. I looked at a music one and it was one of the best visualisations of notes/harmony I've seen
Honestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one
There is mixed consensus on this. But copy-pasting snippets to solve my problems is how I've learned to program so far; why should ML be any different.
The best way to practice is to spend time thinking
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