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

erikbern

from matplotlib import pyplot
import random
import time

pyplot.style.use("ggplot")
now = time.time()

def generate_user(censor=now):
    # Pick some point in time the user was created
    t_created = t = now - random.random() * 1e7

d0choa

library("tidyverse")
library("sparklyr")
library("sparklyr.nested")
library("cowplot")
library("ggsci")

#Spark config
config <- spark_config()

# Allowing to GCP datasets access

hadley

If you were to give recommendations to your "little brother/sister" on things that they need to do to become a data scientist, what would those things be?

I think the "Data Science Venn Diagram" (http://drewconway.com/zia/2013/3/26/the-data-science-venn-diagram) is a great place to start. You need three things to be a good data scientist:

• Statistical knowledge
• Programming/hacking skills
• Domain expertise

Statistical knowledge

samuell

import luigi
import time

class TimeTaskMixin(object):
    '''
    A mixin that when added to a luigi task, will print out
    the tasks execution time to standard out, when the task is
    finished
    '''
    @luigi.Task.event_handler(luigi.Event.PROCESSING_TIME)

electrum

See https://github.com/facebook/presto/wiki/HTTP-Protocol

admackin

_ssh_auth_save() {
    ln -sf "$SSH_AUTH_SOCK" "$HOME/.ssh/ssh-auth-sock.$HOSTNAME"
}
alias screen='_ssh_auth_save ; export HOSTNAME=$(hostname) ; screen'
alias tmux='_ssh_auth_save ; export HOSTNAME=$(hostname) ; tmux'

UniIsland

# Intro

extremely simple and unsophisticated cross process data sharing

supports one read-write master process and an arbitrary number of read-only processes

please consider using pickle/cPickle/ctype to store complex data

# References