rain-1

Purpose

Bootstrap knowledge of LLMs ASAP. With a bias/focus to GPT.

Avoid being a link dump. Try to provide only valuable well tuned information.

Prelude

Neural network links before starting with transformers.

hirrolot

type term =
  | Lam of (term -> term)
  | Pi of term * (term -> term)
  | Appl of term * term
  | Ann of term * term
  | FreeVar of int
  | Star
  | Box

let unfurl lvl f = f (FreeVar lvl)

BekaValentine

import sys
from time import sleep
import random
import cursor



class Renderer:

  def __init__(self, width, height):

fnky

ANSI Escape Sequences

Standard escape codes are prefixed with Escape:

• Ctrl-Key: ^[
• Octal: \033
• Unicode: \u001b
• Hexadecimal: \x1B
• Decimal: 27

Leonidas-from-XIV

module Let_syntax = struct
  let return = Lwt.return
  let (>>=) = Lwt.Infix.(>>=)
  let (>>|) = Lwt.Infix.(>|=)
  module Let_syntax = struct
    let bind m ~f = Lwt.bind m f
  end
end

porglezomp

-- Note: There is a more complete explanation at https://github.com/hwayne/lets-prove-leftpad/tree/master/idris
import Data.Vect

-- `minus` is saturating subtraction, so this works like we want it to
eq_max : (n, k : Nat) -> maximum k n = plus (n `minus` k) k
eq_max  n     Z    = rewrite minusZeroRight n in rewrite plusZeroRightNeutral n in Refl
eq_max  Z    (S _) = Refl
eq_max (S n) (S k) = rewrite sym $ plusSuccRightSucc (n `minus` k) k in rewrite eq_max n k in Refl

-- The type here says "the result is" padded to (maximum k n), and is padding plus the original

keleshev

#! /usr/bin/env ocaml

let fprintf = Format.fprintf

type t =
  | Tag of {name: string; attributes: (string * string) list; body: t list}
  | String of string

let format_attribute f (key, value) = fprintf f " %s=\"%s\"" key value

Drup

type ('ty,'v) t =
  | Nil : ('v, 'v) t
  | Cons : 'a * ('ty, 'v) t -> ('a -> 'ty, 'v) t

let cons x l = Cons (x,l)

let plus1 l = Cons ((),l)

let one x = Cons (x,Nil)

NoraCodes

// Hello! I'm Leo Tindall, the SilverWingedSeraph, and this is a follow-up to my tutorial on
// using match expressions in Rust.

// In my last video, we created a finite state machine to parse and modify some simple markup.
// In this video, we'll make the machine more abstract and more concise.

// We'll start out the same way: defining the four states of the machine.
// However, we'll use a neat trick that Rust
// provides to make things easier later on. We'll ask the compiler to derive the Copy and Clone
// traits on MachineState, so we don't have to worry about borrowing and ownership.

atduskgreg

Interactive Machine Learning

Taught by Brad Knox at the MIT Media Lab in 2014. Course website. Lecture and visiting speaker notes.

• Power to the People: The Role of Humans in Interactive Machine Learning by Knox, Cakmak, Kulesza, Amershi, and Lau
• A Few Useful Things to Know about Machine Learning by Domingos
• Machine Learning that Matters by Wagstaff
• Beyond Concise and Colorful: Learning Intelligible Rules by Pazzani et al.
• [Designing Games with a Purpose] (https://www.cs.cmu.edu/~biglou/GWAP_CACM.pdf) by von Ahn and Dabbish
• [Human Model Evaluation in Interactive Supervised