Hyperparameter Optimization for Machine Learning
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Perustieteiden korkeakoulu |
Master's thesis
Ask about the availability of the thesis by sending email to the Aalto University Learning Centre oppimiskeskus@aalto.fi
Authors
Date
2016-06-14
Department
Major/Subject
Applied Mathematics
Mcode
SCI3016
Degree programme
Master’s Programme in Applied and Engineering Mathematics (N5TeAM)
Language
en
Pages
73+0
Series
Abstract
In the recent years, there have been significant developments in the field of machine learning, with the modern methods like deep learning, significantly overpassing previous state-of-the-art results on a variety of tasks. These modern methods however, come at the cost of increased complexity and require careful tuning of multiple hyperparameters which specify the model. The common practice still is manual tuning of the hyperparameters, making the use of deep learning methods, more of an art than a science. In this thesis, we will explore some of the methods for automated hyperparameter optimization, focusing on the gradient-based approach. The goal is to provide a gentle introduction to this topic, by first providing a solid overview of essential concepts from both optimization and machine learning.Description
Supervisor
Hollanti, CamillaThesis advisor
Raiko, TapaniKeywords
hyperparameter, machine learning, deep learning, optimization, gradient-based