Mathematical Perspective of Machine Learning

TL;DR

This paper explores the mathematical foundations of machine learning, addressing theoretical challenges in function approximation, optimization, network limitations, and RNNs from a rigorous perspective.

Contribution

It provides a mathematical analysis of key machine learning concepts, offering new insights into their theoretical underpinnings and limitations.

Findings

Identifies theoretical challenges in function approximation.

Analyzes gradient descent as an optimization method.

Examines limitations of fixed size neural networks.

Abstract

We take a closer look at some theoretical challenges of Machine Learning as a function approximation, gradient descent as the default optimization algorithm, limitations of fixed length and width networks and a different approach to RNNs from a mathematical perspective.