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Title: A Distributed Implementation of Training the Restricted Boltzmann Machine
Authors: NG, KIN TEK (吳健迪)
Department: Department of Computer and Information Science
Faculty: Faculty of Science and Technology
Issue Date: 2014
Citation: NG, K. T. (2014). A Distributed Implementation of Training the Restricted Boltzmann Machine (Outstanding Academic Papers by Students (OAPS)). Retrieved from University of Macau, Outstanding Academic Papers by Students Repository.
Abstract: In these recent years, deep learning technique becomes very important in the artificial intelligence research, especially in the machine learning field. Deep learning works well in different applications in machine learning such as image, speech, document processing, etc. Since deep learning is related to a lot of mathematical calculations. Some well-known mathematical model running in behind of it, so it is hard to get start as a novice. As most of deep architectures [1], such as, Deep Belief Network (DBN) [2], Deep Boltzmann Machine [3], stacked auto-encoder [4], are related to or based on the Restricted Boltzmann Machine (RBM) [5]. In this report, we are focus on the training process [6] and distributed implementation of training the Restricted Boltzmann Machine, also evaluating the performances of Restricted Boltzmann Machine in distributed environment.
Instructor: Prof. CHEN, CHUN-LUNG
Programme: Bachelor of Science in Software Engineering
Appears in Collections:FST OAPS 2014

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