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.
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.
veekaybee
/ machine_learning_design_patterns.md
Last active
May 11, 2026 19:42
This book is all about patterns for doing ML. It's broken up into several key parts, building and serving. Both of these are intertwined so it makes sense to read through the whole thing, there are very many good pieces of advice from seasoned professionals. The parts you can safely ignore relate to anything where they specifically use GCP. The other issue with the book it it's very heavily focused on deep learning cases. Not all modeling problems require these. Regardless, let's dive in. I've included the stuff that was relevant to me in the notes.
- Machine learning models are not deterministic, so there are a number of ways we deal with them when building software, including setting random seeds in models during training and allowing for stateless functions, freezing layers, checkpointing, and generally making sure that flows are as reproducible as possib
TL;DR:
- The design of both search and recommendations is to find and filter information
- Search is a "recommendation with a null query"
- Search is "I want this", recommendations is "you might like this"
Data science has a really bad reputation recently. Between Facebook's privacy violations , facial scanning at kiosks in restaurants, and racism in algorithms, there are a lot of cases where surveillance, invasion of privacy, and unethical algorithms are dominating the news.
These cases are really important to make public, study, and prevent. But it's just as important to collect examples of good use cases of data science (that are not hyperbolized or PR fluff) so we can focus on those as an industry, and learn about what makes them work, as well.
Have some? Make some? Feel free to leave a comment or edit.
# What is NLTK?
A natural-language processing library written in Python, used for tons of applications, including analyzing [movie and restaurant reviews](http://crowdsourcing-class.org/assignments/downloads/pak-paroubek.pdf).
More on that [here](https://github.com/nltk/nltk/wiki/Sentiment-Analysis).
[Examples](http://www.laurentluce.com/posts/twitter-sentiment-analysis-using-python-and-nltk/) of how to do sentiment analysis in Python.
Note that tweets here are hand-labelled with regards to sentiment.
veekaybee
/ learning_from_data.md
Created
February 1, 2023 17:16
Notes on Learning from Data
Algorithms find the best ways to do things, but they don't explain "how" they came to those conclusions.
This is a common way to formulate ML problems, using target functions that we don't know but we want to approximate and learn.
veekaybee
/ privacy.md
Last active
June 6, 2025 19:25
A work-in-progress post on how to protect your data and privacy online
This episode of Recsperts was transcribed with Whisper from OpenAI, an open-source neural net trained on almost 700 hours of audio. The model includes an encoder-decoder architecture by tokenizing audio into 30-second chunks, normalizing audio samples to the log-Mel scale, and passing the data into an encoder. A decoder is trained to predict the captioned text matching the encoder, and the model includes transcription, as well as timestamp-aligned transcription, and multilingual translation.
The transcription process outputs a single string file, so it's up to the end-user to parse out individual speakers, or run the model [through a sec
veekaybee
/ uninstall_pyenv_mac.md
Created
November 16, 2024 00:47
You might want to use uv now that it's gotten a bit more stable for Mac.
I've already been using it at work and wanted to install it locally for a new project on my computer, but had pyenv.
Only do this if you completely want to rip out pyenv, otherwise, just disable it by removing from your ~/.zshrc
- Comment out everything related to
pyenvin my~/.zshrcfile andsource ~/.zshrc- you may have to search around for all instances if you are like me and not organized about your ~/.zshrc rm -rf "$HOME/.pyenv"# DOUBLE CHECK THIS COMMAND AND WHERE YOUR pyenv isbrew uninstall pyenvjust in case
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