Deep Learning (Partly) Demystified | Zendy

Владик Крейнович | Zendy; Olga Kosheleva | Zendy

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Deep Learning (Partly) Demystified

Author(s) -

Владик Крейнович,

Olga Kosheleva

Publication year - 2020

Publication title -

digitalcommons@utep (the university of texas at el paso)

Language(s) - English

Resource type - Conference proceedings

DOI - 10.1145/3396474.3396481

Subject(s) - pooling , deep learning , computer science , artificial intelligence , heuristic , selection (genetic algorithm) , simple (philosophy) , function (biology) , natural (archaeology) , machine learning , epistemology , philosophy , geography , evolutionary biology , biology , archaeology

Successes of deep learning are partly due to appropriate selection of activation function, pooling functions, etc. Most of these choices have been made based on empirical comparison and heuristic ideas. In this paper, we show that many of these choices - and the surprising success of deep learning in the first place - can be explained by reasonably simple and natural mathematics.

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