Deep learning models, with their vast capacity to fit complex data patterns, are prone to overfitting when trained on limited or noisy datasets. Regularization techniques act as constraints or ...

Deep neural networks (DNNs) can achieve high accuracy when there is abundant training data that has the same distribution as the test data. In practical applications, data deficiency is often a ...

Artificial neural networks, deep-learning methods and the backpropagation algorithm 1 form the foundation of modern machine learning and artificial intelligence. These methods are almost always used ...