Gabriel Alejandro Ceron Viveros
Study and analysis of training strategies to improve the reliability of artificial neural networks.
Rel. Edgar Ernesto Sanchez Sanchez, Annachiara Ruospo. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Informatica (Computer Engineering), 2021
|
Preview |
PDF (Tesi_di_laurea)
- Tesi
Licenza: Creative Commons Attribution Non-commercial No Derivatives. Download (4MB) | Preview |
Abstract
In the latest years we have seen an increased us of machine learning applications due to the increasing computational power and the development of more advanced techniques to train and implement these algorithms. Machine learning applications can be trained with real world data to perform a task without explicit programming. One of the most popular and widely used machine learning algorithm is the artificial neural network (ANN) and specially the deep neural networks (DNNs) which have shown to perform even above human precision. The great performance of DNNs have found successful applications in various areas such as avionics, automotive and medical devices.
Some of these areas are considered safety-critical because system failures can compromise human lives
Relatori
Anno Accademico
Tipo di pubblicazione
Numero di pagine
Corso di laurea
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modifica (riservato agli operatori) |
