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

abhinishetye

100 Days Of ML Code

Hi! I am Abhini, a Machine Learning Enthusiast and this is my log for the 100DaysOfMLCode Challenge

Day 1: July 08, 2018

Today's Progress: Understood the basics of Neural Network and how to build ANN. Also practiced Python on Hackerrank.

Thoughts: Cleared up my concepts on ANN in which I had earlier found confusing like Activation and Cost functions, Batch and Stochastic Gradient Descent and Backpropagation.

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!

revox

# basic scrape and write demonstration used in Goldsmiths digital sandbox 2014
import urllib		# fetches raw web pages for us
import bs4		# turns raw web pages into object hierarchy and provides selectors (like CSS and Xpath does)
import csv		# simplifies the process of writing data to Comma Separated Values in a file

# a list of URLs on YouTube that we want to scrape data from
pagesToScrape = ['http://www.youtube.com/watch?v=9hIQjrMHTv4'
				,'https://www.youtube.com/watch?v=Uk8x3V-sUgU']

# open a file in append mode to write into in the same directory where we ran this script from

evansneath

#!/usr/bin/env python3

def crc(msg, div, code='000'):
    """Cyclic Redundancy Check

    Generates an error detecting code based on an inputted message
    and divisor in the form of a polynomial representation.

    Arguments:
        msg: The input message of which to generate the output code.