A machine learning paradigm where a model is trained on certain tasks and then applied to new, unseen tasks without additional training. It leverages generalizable knowledge to perform well on tasks it has not explicitly encountered during training. (an instance og transfer learning)

From optimization, to convex optimization, to first order optimization, to gradient descent, to accelerated gradient descent, to AdaGrad, to Adam.

This article summarizes our success with DataCamp Donates for the year 2023.

Statistical Modeling: The Two Cultures is an influential essay by Leo Breiman that delineates two approaches to statistical modeling: the "data modeling" culture, which emphasizes formal statistical inference and model fitting, and the "algorithmic modeling" culture, which prioritizes predictive accuracy and computational efficiency. Breiman argues for a shift towards the latter culture, advocating for the development and use of robust algorithms and machine learning techniques that focus on prediction rather than solely on theoretical statistical inference.

dbt is an open-source command line tool that helps analysts and engineers transform data in their warehouse more effectively. dbt is a SQL-first transformation workflow that lets teams quickly and collaboratively deploy analytics code following software engineering best practices like modularity, portability, CI/CD, and documentation. This is my notes on DBT as I prepare for DBT Analytics Engineering certification.

Paper exploration on SMOTE, or Synthetic Minority Over-sampling Technique, which was introduced to tackle class imbalance. Currently, it is widely adopted by practitioners and researchers alike.