This page contains posts under machine learning, computer vision topics I've learned through courses, projects I'm involved in, or from my research on the internet.
In this post, I'll show you how to setup a production-ready MLFlow environment in your local machine. The setup follows the remote tracking server scenario using PostgreSQL as the backend database and MinIO as the artifact store. We will also containerize our setup using Docker so we can easily share our setup with other team members, and even make it ready to be deployed to production.
Gradient descent is an algorithm for finding the local minimum of a differential function by taking repated steps in the opposite direction of the gradient of that function. It has been, by far, the most popular optimisation method for training deep neural networks.
Linear regression is the simplest method for regression analysis. With current state of machine learning and deep learning, we might often overlook linear regression, but it remains a popular method in practice. In this article, we're going through the formulation of linear regression and implement it from scratch in Python.
In this article, we're going through several popular metrics used to evaluate the performance of object detection models for images.
