# name
Analysis of neural networks. Applications to interpretability and uncertainty

# metadata.dc.description[].value
Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2020, Director: Antoni Benseny i Alberto Rubio Muñoz

# metadata.dc.description.abstract[].value
[en] From image creation and pattern recognition to speech and text processing, the outstanding performance of neural networks in a wide variety of fields has made them a popular tool among researchers. However, the fact that we do not fully understand why their performance is so successful or how they operate converts this technology into a black-box model based on trial and error. In this work, we attempt to give deep neural networks a mathematical representation and present different examples and applications that bring light to the understanding of neural networks’ behaviour and usage.

# metadata.dc.rights[].value
cc-by-nc-nd (c) Gabriel Marín Sánchez, 2020

# metadata.dc.source[].value
Treballs Finals de Grau (TFG) - Matemàtiques

# metadata.dc.title[].value
Analysis of neural networks. Applications to interpretability and uncertainty