Recurrent Neural Network / RNN
The type of nervous network that carries the knowledge of the previous steps to capture dependency in the sequence data.
The repeating nervous network (Recurrent Neural Network, RNN), is a type of nervous network designed to capture dependency in sorted data such as text, speech or time series. Unlike standard forward-feeding networks, RNN uses a memory vector called "hidden state" (hidden state) that is not only the current input in every step. Thus, the network may use the information processed in the previous parts of the directory in the next estimates by recalling a nevi, for example, a zamirin in a sentence needs previous words to understand what is referring to.
However, classic RNNs had a serious weakness: gradyans during my resemblance have been lost or exploded throughout the long series or too, (vanishing/exploding gradient problem), which makes it practical to learn long-distance dependency of the network. Although this problem is partially mitigated with advanced RNN variants of door mechanisms such as LSTM and GRU, the main solution has come with the self-extinguishing mechanism of Transformer architecture in 2017, this architecture can train both faster and capture much longer dependency as it provides direct and parallel access to all arrays instead of moving information in order. For this reason RNNs have largely replaced Transformer in today’s large language models, but it continues to create the conceptual basis of row data processing.
However, classic RNNs had a serious weakness: gradyans during my resemblance have been lost or exploded throughout the long series or too, (vanishing/exploding gradient problem), which makes it practical to learn long-distance dependency of the network. Although this problem is partially mitigated with advanced RNN variants of door mechanisms such as LSTM and GRU, the main solution has come with the self-extinguishing mechanism of Transformer architecture in 2017, this architecture can train both faster and capture much longer dependency as it provides direct and parallel access to all arrays instead of moving information in order. For this reason RNNs have largely replaced Transformer in today’s large language models, but it continues to create the conceptual basis of row data processing.