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.

jclosure

swedishmike

## Create the virtual environment
conda create -n 'environment_name'

## Activate the virtual environment
conda activate 'environment_name'

## Make sure that ipykernel is installed
pip install --user ipykernel

## Add the new virtual environment to Jupyter

prof3ssorSt3v3

/*
*********************************************************************
http://www.mysqltutorial.org
*********************************************************************
Name: MySQL Sample Database classicmodels
Link: http://www.mysqltutorial.org/mysql-sample-database.aspx
Version 3.2
+ changed the order of the CREATE TABLE and ALTER TABLE commands to work with MySQL 5.7
  and issues with FOREIGN KEY CONSTRAINTS
Version 3.1

d6tdev

4 Reasons Why Your Machine Learning Code is Probably Bad

Your current workflow probably chains several functions together like in the example below. While quick, it likely has many problems:

• it doesn't scale well as you add complexity
• you have to manually keep track of which functions were run with which parameter
• you have to manually keep track of where data is saved
• it's difficult for others to read

joaofig

from shapely.geometry import Point
from functools import partial
from shapely.ops import transform
import pyproj

def buffer_in_meters(lng, lat, radius):
    proj_meters = pyproj.Proj(init='epsg:3857')
    proj_latlng = pyproj.Proj(init='epsg:4326')
    
    project_to_meters = partial(pyproj.transform, proj_latlng, proj_meters)

ericmjl

UPDATE: I have baked the ideas in this file inside a Python CLI tool called pyds-cli. Please find it here: https://github.com/ericmjl/pyds-cli

How to organize your Python data science project

Having done a number of data projects over the years, and having seen a number of them up on GitHub, I've come to see that there's a wide range in terms of how "readable" a project is. I'd like to share some practices that I have come to adopt in my projects, which I hope will bring some organization to your projects.

Disclaimer: I'm hoping nobody takes this to be "the definitive guide" to organizing a data project; rather, I hope you, the reader, find useful tips that you can adapt to your own projects.

Disclaimer 2: What I’m writing below is primarily geared towards Python language users. Some ideas may be transferable to other languages; others may not be so. Please feel free to remix whatever you see here!

ledmaster

BrikerMan

from gensim.models import KeyedVectors

# Load gensim word2vec
w2v_path = '<Gensim File Path>'
w2v = KeyedVectors.load_word2vec_format(w2v_path)

import io

# Vector file, `\t` seperated the vectors and `\n` seperate the words
"""

deehzee

Module autoreload

To auto-reload modules in jupyter notebook (so that changes in files *.py doesn't require manual reloading):

# for auto-reloading external modules
# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython
%load_ext autoreload
%autoreload 2