Hi! Hope you’re in a good mood today. In this tutorial we are going to learn about K-fold cross validation and how to implement it in python, let’s begin with a brief introduction: Suppose you build a machine learning model to solve a problem, you split the dataset to train and test sets and then,    

There is a very elegant technique to remove crowds from photos in which you just use a tripod and take multiple photos from the exact same spot. Every single picture might have tons of people, but every picture has people in different spots! So if you just average the pixels most of the crowd will just disappear. (Technically, the median works better, but I’m getting ahead of myself).

This post is related to the recent release of a new open-source project called OpenMetricLearning (OML), and one of its goals is to lower the entry threshold for metric learning pipelines. We will briefly introduce the theory, discuss the examples in code and show how simple heuristics can perform on a level comparable with the current SotA. Since the project is new, each star on GitHub is essential for us.