Algorithmic Learning and Deep Representation Learning

Abstract

Abstract: Artificial intelligence is now a critical resource in engineering and experimental science, comparable to statistics and calculus. As data science grows, its foundations—AI, machine learning, and deep learning—are paramount. This paper explores their interconnections. Machine learning is a prerequisite for most analytical tasks. We present an introductory explanation of machine learning and focus on deep learning as its contemporary evolution, describing its core architecture. A comparison between the two approaches provides researchers with a broad overview to guide the choice of the optimal solution for a given challenge.