veekaybee

ChatGPT Resources

Context

ChatGPT appeared like an explosion on all my social media timelines in early December 2022. While I keep up with machine learning as an industry, I wasn't focused so much on this particular corner, and all the screenshots seemed like they came out of nowhere. What was this model? How did the chat prompting work? What was the context of OpenAI doing this work and collecting my prompts for training data?

I decided to do a quick investigation. Here's all the information I've found so far. I'm aggregating and synthesizing it as I go, so it's currently changing pretty frequently.

Model Architecture

margaretmeehan

def unet(input_shape):
    '''
    Params: input_shape -- the shape of the images that are input to the model
                           in the form (width_or_height, width_or_height,
                           num_color_channels)

    Returns: model -- a model that has been defined, but not yet compiled.
                      The model is an implementation of the Unet paper
                      (https://arxiv.org/pdf/1505.04597.pdf) and comes
                      from this repo https://github.com/zhixuhao/unet. It has

geffy

# -*- coding: utf-8 -*-
"""
Created on Mon Sep 23 23:16:44 2017

@author: Marios Michailidis

This is an example that performs stacking to improve mean squared error
This examples uses 2 bases learners (a linear regression and a random forest)
and  linear regression (again) as a meta learner to achieve the best score.
The initial train data are split in 2 halves to commence the stacking. 

genekogan

from bs4 import BeautifulSoup
import requests
import re
import urllib2
import os
import argparse
import sys
import json

# adapted from http://stackoverflow.com/questions/20716842/python-download-images-from-google-image-search

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

BenElgar

import urllib, StringIO
from math import log, exp, tan, atan, pi, ceil
from PIL import Image

EARTH_RADIUS = 6378137
EQUATOR_CIRCUMFERENCE = 2 * pi * EARTH_RADIUS
INITIAL_RESOLUTION = EQUATOR_CIRCUMFERENCE / 256.0
ORIGIN_SHIFT = EQUATOR_CIRCUMFERENCE / 2.0

def latlontopixels(lat, lon, zoom):

karpathy

"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

dynamicguy

# install dependencies
sudo apt-get update
sudo apt-get install -y build-essential
sudo apt-get install -y cmake
sudo apt-get install -y libgtk2.0-dev
sudo apt-get install -y pkg-config
sudo apt-get install -y python-numpy python-dev
sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install -y libjpeg-dev libpng-dev libtiff-dev libjasper-dev
 

46bit

import sys, cv2

# Refactored https://realpython.com/blog/python/face-recognition-with-python/

def cascade_detect(cascade, image):
  gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  return cascade.detectMultiScale(
    gray_image,
    scaleFactor = 1.15,
    minNeighbors = 5,

MohamedAlaa

tmux shortcuts & cheatsheet

start new:

tmux

start new with session name:

tmux new -s myname