Castelli, MauroBakurov, IllyaMartins, Guilherme de Oliveira Crespo2021-11-192021-11-192021-11-09http://hdl.handle.net/10362/127958Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsDeep 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.engMachine learningDeep neural networksParticles warmo ptimizationGradient descentComputer visionmaster thesis202792617