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tfg_marina_mallol_blay.pdf (5.1 MB)

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Bachelor thesis

Publication date

2021-06-20

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cc-by-nc-nd (c) Marina Mallol Blay, 2021
Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/185454

L'algorisme de retropropagació d'errors des d'un punt de vista matemàtic

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Abstract

[en] In this project, we are going to study the backpropagation algorithm from a mathematical perspective and analize why this algorithm, which was forgotten in the past, is now used in supervised learning in order to train artificial neural networks. First of all we will talk about how artificial neural networks are organized and how they work. Next, we will study the gradient descend method, the Newton’s method, and other methods, used in learning. Finally, we will see how the backpropagation algorithm works and the calculations derived from it.

Description

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2021, Director: Miquel Bosch Gual

Subject

Xarxes neuronals (Informàtica), Treballs de fi de grau, Aprenentatge automàtic, Algorismes computacionals

Subject (English)

Neural networks (Computer science), Bachelor's theses, Machine learning, Computer algorithms

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Treballs Finals de Grau (TFG) - Matemàtiques

Citation

MALLOL BLAY, Marina. L'algorisme de retropropagació d'errors des d'un punt de vista matemàtic. [consulted: 15 of June of 2026]. Available at: https://hdl.handle.net/2445/185454

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