Two of the main techniques used in order to try to discover causal relationships are Graphical Methods (such as Knowledge Graphs and Bayesian Belief Networks) and Explainable AI. These two methods form in fact the basis of the Association level in the Causality Hierarchy (Figure 1),... Read more

Gradient descent is one of the first concepts many learn when studying machine or deep learning. This optimization algorithm underlies most of machine learning, including backpropagation in neural networks. When learning gradient descent, we learn that learning rate and batch size matter. Specifically, increasing the learning rate speeds... Read more

K Nearest Neighbors is one of the simplest, if not the simplest, machine learning algorithms. It is a classification algorithm that makes predictions based on a defined number of nearest instances. Today you’ll get your hands dirty by implementing and tweaking the K nearest neighbors algorithm... Read more