A Survey on Empirical Theory of Deep Learning

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Motesharei, Erfan | 2023

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Type of Document: M.Sc. Thesis
Language: Farsi
Document No: 56763 (02)
University: Sharif University of Technology
Department: Mathematical Sciences
Advisor(s): Foroughmand Araabi, Mohammad Hadi
Abstract:
The aim of this thesis is to review the theory of deep learning with an experimental approach. In this thesis, we review researches that examine the impact of input selection on outputs in deep learning systems; Inputs we can control (samples, architecture, model size, optimizer, etc.) and outputs we can observe (the performance of the neural network, its test error, its parameters, etc.). Among the reviewed cases are the generalizability of deep learning systems, the effect of model components on its accuracy, interpolation and hyperparameters, as well as new phenomena in this field for which new frameworks have been defined
Keywords:
Deep Learning ; Empirical Investigation Relations ; Generalization ; Double Descent