Garrido Beltrán, LluísEspuña Fontcuberta, Aleix2019-10-012019-10-012019-06https://hdl.handle.net/2445/141424Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2019, Tutor: Lluís Garrido BeltránThe goal of this project was to develop with Python a classiffer capable to separate two different classes (binary classiffer). To do this, we implemented a neural network that uses gradient descent with the backpropagation algorithm to learn in a supervised training process. We first trained the net and studied its behavior with a binary classification problem that we created, generating our own training examples with the acceptance-rejection algorithm. We realized that the net was returning the conditional probability for each example to belong to one of the classes. After that, we focused on two different real classification problems, both obtained from available online machine learning data sets.5 p.application/pdfengcc-by-nc-nd (c) Espuña, 2019http://creativecommons.org/licenses/by-nc-nd/3.0/es/Aprenentatge automàticXarxes neuronals (Informàtica)Python (Llenguatge de programació)Treballs de fi de grauMachine learningNeural networks (Computer science)Python (Computer program language)Bachelor's thesesinfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/openAccess