Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or spoken words. They excel at tasks involving temporal dynamics and have been successfully applied in various domains including computer vision for tasks like object tracking, action recognition, and video captioning. RNNs maintain a hidden state that encodes information about previous inputs, which allows them to capture dependencies and context present in sequences. This unique ability differentiates RNNs from other neural network architectures, making them indispensable tools for addressing complex computer vision problems where context and temporal relationships are crucial.