An implementation of Deep Belief Networks using restricted Boltzmann machines in Clojure
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.^
James Christopher Sims,
"An implementation of Deep Belief Networks using restricted Boltzmann machines in Clojure"
Dissertations and Master's Theses (Campus Access).