A Recurrent Neural Network (RNN) is a type of artificial neural network used to process sequential or temporal data. Unlike feedforward neural networks, in which information flows in one direction only, in RNNs information flows in a loop, i.e. the output at one point in time is used as input at the next point in time.
The ability to process data streams makes them useful for a wide variety of applications, such as speech recognition, machine translation and text generation. One of the main features of RNNs is their ability to model long-term dependencies in data streams.
RNNs have a recurrent structure, which allows information to flow from one layer to another through a hidden state, which stores information about previous states. The hidden state is updated at each time step and is used to influence the output at the next time step. This allows the RNN to have long-term memory and be able to capture patterns in data sequences that extend over time.
Today, consumers of any type of product or service have become demanding. It has been a long time since they were served anything [...]
Read More »The acquisition of new potential customers is one of the most important and difficult processes for a company. Traditionally, it has been necessary to [...]
Read More »In recent years, all topics related to Artificial Intelligence (AI) have been arousing enormous interest. Perhaps it is because the heart of [...]
Read More »More and more companies are taking advantage of the relevant information they extract from the data they possess and generate to improve their processes and discover new ways to [...]
Read More »
