Publication: Optimization Methods In Training Neural Networks
dc.contributor.author | Sathasivam, Saratha | |
dc.date.accessioned | 2025-03-02T06:42:03Z | |
dc.date.available | 2025-03-02T06:42:03Z | |
dc.date.issued | 2003-07 | |
dc.description.abstract | In steepest descent methods, we construct a functional which when extremized will deliver a solution. The function will have convexity properties, so that the vector that extremizes the function is the solution of the algebraic problem in question. This means the search for the vector for which the gradient of the function is zero can be done in an iterative fashion. A special steepest descent method which is appropriate for the solution of the linear algebraic problem is the 'Conjugate Gradient Method'. | |
dc.identifier.uri | https://erepo.usm.my/handle/123456789/21211 | |
dc.language.iso | en | |
dc.subject | Mathematical optimization | |
dc.title | Optimization Methods In Training Neural Networks | |
dc.type | Resource Types::text::thesis::master thesis | |
dspace.entity.type | Publication | |
oairecerif.author.affiliation | Universiti Sains Malaysia |