veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models

antoinebrl

Preparation of ImageNet (ILSVRC2012)

The dataset can be found on the official website if you are affiliated with a research organization. It is also available on Academic torrents.

This script extracts all the images and group them so that folders contain images that belong to the same class.

1. Download the ILSVRC2012_img_train.tar and ILSVRC2012_img_val.tar
2. Download the script wget https://gist.githubusercontent.com/antoinebrl/7d00d5cb6c95ef194c737392ef7e476a/raw/dc53ad5fcb69dcde2b3e0b9d6f8f99d000ead696/prepare.sh
3. Run it ./prepare.sh

BIGBALLON

#!/bin/bash
#
# script to extract ImageNet dataset
# ILSVRC2012_img_train.tar (about 138 GB)
# ILSVRC2012_img_val.tar (about 6.3 GB)
# make sure ILSVRC2012_img_train.tar & ILSVRC2012_img_val.tar in your current directory
#
#  https://github.com/facebook/fb.resnet.torch/blob/master/INSTALL.md
# 
#  train/

benagricola

from PyInstaller.utils.hooks import collect_submodules, collect_data_files

# This hooks the scrapy project 'cot' to import all submodules, change name to match scrapy project
hiddenimports = (collect_submodules('cot'))

mlorant

REGIONS = {
    'Auvergne-Rhône-Alpes': ['01', '03', '07', '15', '26', '38', '42', '43', '63', '69', '73', '74'],
    'Bourgogne-Franche-Comté': ['21', '25', '39', '58', '70', '71', '89', '90'],
    'Bretagne': ['35', '22', '56', '29'],
    'Centre-Val de Loire': ['18', '28', '36', '37', '41', '45'],
    'Corse': ['2A', '2B'],
    'Grand Est': ['08', '10', '51', '52', '54', '55', '57', '67', '68', '88'],
    'Guadeloupe': ['971'],
    'Guyane': ['973'],
    'Hauts-de-France': ['02', '59', '60', '62', '80'],

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

oscarperpinan

### DATA SECTION

library(data.table)

## Read data with 'data.table::fread'
input <- fread("wu03ew_v1.csv", select = 1:3)
setnames(input, 1:3, new = c("origin", "destination","total"))
## Coordinates
centroids <- fread("msoa_popweightedcentroids.csv")
## 'Code' is the key to be used in the joins

tmramalho

import theano
import theano.tensor as T
import numpy as np
import cPickle
import random
import matplotlib.pyplot as plt

class RNN(object):

	def __init__(self, nin, n_hidden, nout):

thriveth

CB_color_cycle = ['#377eb8', '#ff7f00', '#4daf4a',
                  '#f781bf', '#a65628', '#984ea3',
                  '#999999', '#e41a1c', '#dede00']

chilts

var request = require('request');
var unzip   = require('unzip');
var csv2    = require('csv2');

request.get('http://s3.amazonaws.com/alexa-static/top-1m.csv.zip')
    .pipe(unzip.Parse())
    .on('entry', function (entry) {
        entry.pipe(csv2()).on('data', console.log);
    })
;