Sharing insights about machine learning, software development, and technology. Thoughts on basics of AI, building AI applications, and engineering best practices.
Explains how a model's goal is to generalize to new data rather than memorize noise. Compares overfitting w underfitting, and how bias/variance play a role in the model's performance.
Outlines the key metrics for assessing regression models beyond simple accuracy. Compares metrics like R-Squared (R²), Mean Squared Error(MSE) Mean Absolute Error (MAE), and Root MSE (RMSE).
Breaks down version control concepts using a relatable analogy: multiple friends working on the same racing game.
Discusses Python's dynamic typing and how type hints bridge the gap with static languages.