Rekurrentvõrgud

Rekurrentvõrgud, also known as recurrent neural networks (RNNs), are a class of artificial neural networks designed to recognize patterns in sequences of data, such as time series or natural language. Unlike traditional feedforward neural networks, RNNs have connections that form directed cycles, allowing them to maintain a form of memory. This makes them particularly well-suited for tasks involving sequential data, such as speech recognition, language modeling, and time series prediction.

The fundamental building block of an RNN is the recurrent unit, which typically consists of a neural network layer followed by a recurrent connection that feeds the output back into the input. This recurrent connection allows the network to process sequences of inputs, where each input depends on the previous ones. However, standard RNNs can suffer from issues like vanishing and exploding gradients, which can make them difficult to train on long sequences.

To address these issues, several variants of RNNs have been developed. Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) are two popular variants that include mechanisms for better handling long-term dependencies. LSTMs use a more complex architecture with gates that control the flow of information, while GRUs use a simpler architecture with fewer gates but still achieve comparable performance.

RNNs have been successfully applied to a wide range of tasks, including natural language processing, speech recognition, and time series forecasting. However, they are not without their limitations. Training RNNs can be computationally intensive, and they may struggle with very long sequences due to issues like vanishing gradients. Despite these challenges, RNNs remain a powerful tool in the field of machine learning and continue to be an active area of research.