Artificial Intelligence: “Predictions with the use of Neural Networks”

CLC’s team, led by Mr. Fotis Sarantopoulos, BSc, Naval Architecture & Marine Engineering NTUA, INSEAD MiM ’23, mentored and supervised our students Natalia Hatzigeorgiou, Emmanuel Kontos, Niki Mazioti, Ilias Sohos, and Perseus Georgiadis, to carry out a research project on AI and Neural Networks.

Artificial intelligence has been a prominent scientific field for many decades. Machine learning is a subset of artificial intelligence, while artificial neural networks are a type of machine learning. Neural networks are based on how the human brain works and have become popular for problems of classification, clustering, pattern recognition, and regression problems. In turn, deep learning can be considered as s subset of machine learning. In deep learning, more complex ways of connecting the layers of the neural network are observed, more neurons are encountered, and more computational power is needed, and even the possibility of automatic feature extraction is present. Deep machine learning algorithms find great application in big data management with notable success in voice recognition, pattern recognition, computer vision, natural language processing, recommendation systems, etc. the aim of this paper is to explore the History of Neural Networks, the Types of Neural networks (Feedforward, Convolutional & Recurrent Neural Networks), and the Issues of Neural Networks (Overfitting & Underfitting).

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