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Single layer optimization with Particle Swarm Optimization: A new approach to optimize Deep Neural Networks
| Nome: | Descrição: | Tamanho: | Formato: | |
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| 2.68 MB | Adobe PDF |
Orientador(es)
Resumo(s)
Deep Neural Networks attempt to simulate the behaviour of the brain to solve complex problems.Today, they are currently used for various real world application ssuch as natural language processing, image recognition, self-driving cars,and much more. However, these models, can
be very computationally expensive and take a considerable amount of time to train. In this
thesis, we attempt to use swarm intelligence to optimize Deep Neural Networks with a smaller
computational budget. To achieve this goal, we implement a method that takes any model and
selects the layer that can contribute the most for the optimization of said model. Afterwards,
we further optimize the layer selected with the Particle Swarm Optimization algorithm in an
attempt to take advantage of its ability to surpass local optimums.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
Palavras-chave
Machine learning Deep neural networks Particles warmo ptimization Gradient descent Computer vision