Artificial Neural Networks and Deep Learning: A Review of Concepts, Architectures, Advances and Applications

Abstract

Artificial neural networks (ANNs) and deep learning are widely used to identify patterns in complex data. This review describes the development of neural networks and explains key learning methods and architectures, including feed-forward networks, convolutional neural networks, recurrent neural networks, restricted Boltzmann machines, deep belief networks, and attention-based models. It also discusses advances such as transfer learning, generative modeling, and deep reinforcement learning, along with applications in computer vision, language processing, medical imaging, and other fields. Despite their strong performance in many tasks, deep learning models can require large datasets and substantial computing resources. Their reliability may also be affected by data quality, bias, limited interpretability, and changes in the conditions in which they are used. Careful evaluation and selection of an appropriate architecture are therefore essential for developing useful and dependable applications.