An implementation of Deep Belief Networks using restricted Boltzmann machines in Clojure

James Christopher Sims, University of Rhode Island

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.^

Subject Area

Computer science

Recommended Citation

James Christopher Sims, "An implementation of Deep Belief Networks using restricted Boltzmann machines in Clojure" (2016). Dissertations and Master's Theses (Campus Access). Paper AAI10006856.
http://digitalcommons.uri.edu/dissertations/AAI10006856

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