bishboria

These links no longer work. Springer have pulled the free plug.

Graduate texts in mathematics

duplicates = multiple editions

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen

yoavg

Reinforcement Learning for Language Models

Yoav Goldberg, April 2023.

Why RL?

With the release of the ChatGPT model and followup large language models (LLMs), there was a lot of discussion of the importance of "RLHF training", that is, "reinforcement learning from human feedback". I was puzzled for a while as to why RL (Reinforcement Learning) is better than learning from demonstrations (a.k.a supervised learning) for training language models. Shouldn't learning from demonstrations (or, in language model terminology "instruction fine tuning", learning to immitate human written answers) be sufficient? I came up with a theoretical argument that was somewhat convincing. But I came to realize there is an additional argumment which not only supports the case of RL training, but also requires it, in particular for models like ChatGPT. This additional argument is spelled out in (the first half of) a talk by John Schulman from OpenAI. This post pretty much

laggardkernel

Comparison of ZSH frameworks and plugin managers

Changelog

• update 1: add a FAQ section
• update 2: benchmark chart and feature comparison table
• update 3:
  • improve the table with missing features for antigen
  • new zplg times result

TLDR

beniwohli

# original XML at http://www.w3.org/Math/characters/unicode.xml
# XSL for conversion: https://gist.github.com/798546


unicode_to_latex = {
    u"\u0020": "\\space ",
    u"\u0023": "\\#",
    u"\u0024": "\\textdollar ",
    u"\u0025": "\\%",
    u"\u0026": "\\&",

endolith

# https://github.com/matplotlib/matplotlib/issues/881
# Several of the ColorBrewer maps are "qualitative", meaning
# they are just a group of colors that can be used together 
# for categories of data.  So I remapped Accent to segments 
# instead of continuous:

# Actually, these should be used with ListedColormap, and 
# the number of colors should depend on the number of 
# categories in the data, with colors removed from the 
# list in a certain order?

fizruk

-- A Pandoc filter to use Pygments for Pandoc
-- Code blocks in HTML output
-- Nickolay Kudasov 2013
-- Requires Pandoc 1.12

import Text.Pandoc.Definition
import Text.Pandoc.JSON (toJSONFilter)
import Text.Pandoc.Shared
import Data.Char(toLower)
import System.Process (readProcess)

jcheng5

library(ggplot2)
library(shiny)

# Call ggbrush with a ggplot2 object, and the dimensions which
# should be brushed (try "xy" for scatter, "x" for histogram).
# The plot will show in RStudio Viewer or your web browser, and
# any observations selected by the user will be returned.
ggbrush <- function(plotExpr, direction = c("xy", "x", "y")) {
  
  # See below for definition of dialogPage function

hrbrmstr

hadley

I think the two most important messages that people can get from a short course are:

a) the material is important and worthwhile to learn (even if it's challenging), and b) it's possible to learn it!

For those reasons, I usually start by diving as quickly as possible into visualisation. I think it's a bad idea to start by explicitly teaching programming concepts (like data structures), because the pay off isn't obvious. If you start with visualisation, the pay off is really obvious and people are more motivated to push past any initial teething problems. In stat405, I used to start with some very basic templates that got people up and running with scatterplots and histograms - they wouldn't necessary understand the code, but they'd know which bits could be varied for different effects.

Apart from visualisation, I think the two most important topics to cover are tidy data (i.e. http://www.jstatsoft.org/v59/i10/ + tidyr) and data manipulation (dplyr). These are both important for when people go off and apply

tokestermw

"""
preprocess-twitter.py

python preprocess-twitter.py "Some random text with #hashtags, @mentions and http://t.co/kdjfkdjf (links). :)"

Script for preprocessing tweets by Romain Paulus
with small modifications by Jeffrey Pennington
with translation to Python by Motoki Wu

Translation of Ruby script to create features for GloVe vectors for Twitter data.