huyng

### MATPLOTLIBRC FORMAT

# This is a sample matplotlib configuration file - you can find a copy
# of it on your system in
# site-packages/matplotlib/mpl-data/matplotlibrc.  If you edit it
# there, please note that it will be overridden in your next install.
# If you want to keep a permanent local copy that will not be
# over-written, place it in HOME/.matplotlib/matplotlibrc (unix/linux
# like systems) and C:\Documents and Settings\yourname\.matplotlib
# (win32 systems).

pfmoore

data = {}
data["extend"] = function (data, t)
    for n, recipe in ipairs(t) do
        for i, component in ipairs(recipe["ingredients"]) do
            cname = component[1] or component["name"]
            camt = component[2] or component["amount"]
            print('"' .. recipe["name"] .. '","' .. cname .. '",' .. camt)
        end
    end
end

iamtekeste

Download Google Drive files with WGET

Example Google Drive download link:

https://docs.google.com/open?id=[ID]
To download the file with WGET you need to use this link:

https://googledrive.com/host/[ID]

Example WGET command:

subfuzion

# Inspirations:
# http://mutelight.org/practical-tmux
# http://zanshin.net/2013/09/05/my-tmux-configuration/
# http://files.floriancrouzat.net/dotfiles/.tmux.conf
# http://stackoverflow.com/questions/9628435/tmux-status-bar-configuration
# https://github.com/Lokaltog/powerline
# https://github.com/remiprev/teamocil
# http://superuser.com/questions/74492/whats-the-best-prefix-escape-sequence-for-screen-or-tmux
# http://blog.hawkhost.com/2010/07/02/tmux-%E2%80%93-the-terminal-multiplexer-part-2/
#

dmofot

Table of Contents

• TL;DR
• General
  • Method Information:
  • Benchmarking Information:
  • Postgres/PostGIS Information:
• Create Benchmark Function
• Tests

sorja

// OFF    = 0
// WARN   = 1
// ERROR  = 2;

{
  "env": {
    "browser": true,
    "node": true,
    "es6": true
  },

mohit-sinha

import pandas as pd;
import numpy as np;
import lightgbm as lgb
from bayes_opt import BayesianOptimization
from sklearn.model_selection import cross_val_score

def lgb_evaluate(                
                numLeaves,
                maxDepth,
                scaleWeight,

jph00

sudo add-apt-repository -y ppa:apt-fast/stable
sudo add-apt-repository -y ppa:graphics-drivers/ppa
sudo apt-get update
sudo apt-get -y install apt-fast
# prompts

sudo apt-fast -y upgrade

sudo apt-fast install -y python3-pip ubuntu-drivers-common libvorbis-dev libflac-dev libsndfile-dev cmake build-essential libgflags-dev libgoogle-glog-dev libgtest-dev google-mock zlib1g-dev libeigen3-dev libboost-all-dev libasound2-dev libogg-dev libtool libfftw3-dev libbz2-dev liblzma-dev libgoogle-glog0v5 gcc-6 gfortran-6 g++-6 doxygen graphviz libsox-fmt-all parallel exuberant-ctags vim-nox python-powerline python3-pip ack lsyncd
sudo apt-fast install -y tigervnc-standalone-server firefox mesa-common-dev

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.