Any fundamental discovery involves a significant degree of risk. If an idea is guaranteed to work then it moves from the realm of research to engineering. Unfortunately, this also means that most…
Principal component analysis(PCA) is one of the key algorithms that are part of any machine learning curriculum. Initially created in the early 1900s, PCA is a fundamental algorithm to understand…
In this article, we’re going to introduce self-organizing maps. We assume the reader has prior experience with neural networks. Self-organizing maps are a class of unsupervised learning neural…
In a broader mathematical or computational perspective, an optimization problem is defined as a problem of finding the best solution from all feasible solutions. In terms of Machine Learning and…
In this blog post we will begin to look at Monte Carlo methods and how they can be used. These form the backbone of (essentially) all statistical computer modelling.