In the last decade, Neural Networks (NNs) have come to the fore as one of the most powerful and versatile approaches to many machine learning tasks. Deep Learning (DL), the latest incarnation of NNs, is nowadays applied in every scenario that needs models able to predict or classify data. From computer vision to speech-to-text, DL techniques are able to achieve super-human performance in many cases. This chapter is devoted to give a (not comprehensive) introduction to the field, describing the main branches and model architectures, in order to try to give a roadmap of this area to the reader.

Neural Networks and Deep Learning Fundamentals

Riccardo Zese
Primo
;
Elena Bellodi
Secondo
;
Michele Fraccaroli;Fabrizio Riguzzi
Penultimo
;
Evelina Lamma
Ultimo
2022

Abstract

In the last decade, Neural Networks (NNs) have come to the fore as one of the most powerful and versatile approaches to many machine learning tasks. Deep Learning (DL), the latest incarnation of NNs, is nowadays applied in every scenario that needs models able to predict or classify data. From computer vision to speech-to-text, DL techniques are able to achieve super-human performance in many cases. This chapter is devoted to give a (not comprehensive) introduction to the field, describing the main branches and model architectures, in order to try to give a roadmap of this area to the reader.
2022
978-3-031-03841-9
Deep Learning, Neural Networks, Hyper-parameters auto tuning
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/2501381
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