Date of Award

2017

Document Type

Open Access Thesis

Department

Mathematics

Sub-Department

College of Arts and Sciences

First Advisor

Edsel Peña

Abstract

In this paper we describe and survey the field of deep learning, a type of machine learning that has seen tremendous growth and popularity over the past decade for its ability to substantially outperform other learning methods at important tasks. We focus on the problem of supervised learning with feedforward neural networks. After describing what these are we give an overview of the essential algorithms of deep learning, backpropagation and stochastic gradient descent. We then survey some of the issues that occur when applying deep learning in practice. Last, we conclude with an important application of deep learning to the problem of handwriting recognition.

Included in

Mathematics Commons

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