Date of Award

2016

Degree Type

Thesis

Degree Name

Master of Science in Computer Science

Department

Computer Science and Statistics

First Advisor

Lutz Hamel

Abstract

In a work that ultimately heralded a resurgence of deep learning as a viable and successful machine learning model, Dr. Geoffrey Hinton described a fast learning algorithm for Deep Belief Networks. This study explores that result and the underlying models and assumptions that power it.

The result of the study is the completion of a Clojure library (deebn) implementing Deep Belief Networks, Deep Neural Networks, and Restricted Boltzmann Machines. deebn is capable of generating a predictive or classification model based on varying input parameters and dataset, and is available to a wide audience of Clojure users via Clojars, the community repository for Clojure libraries. These capabilities were not present in a native Clojure library at the outset of this study.

deebn performs quite well on the reference MNIST dataset with no dataset modification or hyperparamter tuning, giving a best performance in early tests of a 2.00% error rate.

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