Rekureentsi

Rekureentsi, 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 memory enables RNNs to process sequences by maintaining a hidden state that gets updated as the network processes each element in the sequence.

The basic architecture of an RNN consists of an input layer, a hidden layer, and an output layer. The hidden layer contains recurrent connections, which means that the output of the hidden layer at a given time step is fed back into the network as input for the next time step. This recurrent structure allows RNNs to capture temporal dependencies and context within the sequence data.

RNNs have been successfully applied to various tasks, including language modeling, speech recognition, and machine translation. However, traditional RNNs suffer from issues such as the vanishing gradient problem, which makes it difficult for the network to learn long-term dependencies. To address this limitation, more advanced architectures like Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs) have been developed. These variants incorporate gating mechanisms that help regulate the flow of information, allowing the network to better capture and retain long-term dependencies.

Despite their effectiveness, RNNs can be computationally intensive and may require significant resources for training, especially for large-scale applications. Ongoing research continues to explore optimizations and alternative architectures to improve the efficiency and performance of recurrent neural networks.