[en] In this project we study antoencoders, a machine learning tecnique used for dimensionality reduction of databases, analizing images or generating new data. We compare them with tradicional dimensionality reduction method, the principal component analysis (PCA). Even though in some fields (specially with small databases) PCA is useful we show that autoencoders can accomplish the same tasks with better results and even accomplish new ones unattainable with PCA. We prepared programs in Python implementing several versions of autoencoders, applied frequently used databases, comparing results with those obtained with PCA, when applicable.

Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2023, Director: Josep Fortiana Gregori