Recurrent

Recurrent neural networks (RNNs) are a type of neural networks that are capable of learning from data obtained in the past. They are used to solve problems related to natural language processing, speech recognition, computer vision and other areas.

Unlike conventional neural networks (NN), RNNs have a special architecture that allows them to remember information received in the past and use it to make decisions in the future. This is achieved by adding a recurrent block to the network, which stores information about previous states of the network and transmits it to subsequent layers.

A recurrent block consists of several elements, such as memory cells, which can store information about previous input data. Each element of a memory cell has access to information about previous states and can use this information to make a decision in the current state.

Due to their ability to remember information, RNNs can process sequences of data, such as sequences of words in text or time series of data. They can also be used to predict future values ​​based on previous data.

One of the main advantages of RNNs is that they can process data in real time, which makes them useful for applications that require fast data processing, such as speech recognition or financial data processing.

A recursive method is a function (in a programming context) that can be called repeatedly. For example, to solve problems of traversing a graph or solving equations. A recursive program (or recursive algorithms) uses direct or indirect recursion to solve a problem. This may result in unexpectedly high memory usage