A collection of my thoughts, and learnings from AI, quantum computing, and neuroscience.
NLP
Giving attention to transformers
Generative pre-trained transformer
General
Feature engineering for tabular data
Correlation coefficient intuition & machine learning
The universal approximation theorem from scratch
https://medium.com/future-vision/a-simple-introduction-to-the-basics-of-data-science-cdeaf237c4b9
Exploration
AI is in a local max and the brain can help
Can current LLMs discover novel ideas?
Understanding neural networks (but actually)
Can we measure how good models are at recursively self improving?