This article, written by Max Charney and originally published on Towards AI, delves into the concept of dimensions in machine learning and deep learning. It explores how dimensions play a role in machine learning, particularly in the context of feature spaces. The article discusses the use of dimensions in images and model learning, as well as the applications of high dimensional feature spaces in real-world scenarios, such as unsupervised learning and recommendation systems. The author also provides examples and visualizations to illustrate the relevance of dimensions in machine learning.
source update: What Are Dimensions in Machine Learning? – Towards AI