Functional parallel programming techniques for feed-forward artificial neural networks trained using backpropagation learning are analyzed. In particular, the Data Parallel Haskell extension to the Glasgow Haskell Compiler is considered as a tool for achieving data parallelism. We find much potential and elegance in this method, and determine that a sufficiently large workload is critical in achieving real gains. Several additional features are recommended to increase usability and improve results on small datasets. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/ETD-UT-2009-08-280 |
| Date | 2009 August 1900 |
| Creators | Heartsfield, Gregory Lynn |
| Source Sets | University of Texas |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
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