Description: Generative Adversarial Networks (GAN) are a type of neural network architecture used to generate new data from an existing dataset. In particular, Recurrent Neural Network GANs (RNN GANs) combine the capabilities of GANs with RNNs, which are especially effective at handling sequential data, such as text or time series. In this context, an RNN GAN consists of two main components: the generator and the discriminator. The generator is responsible for creating new data sequences, while the discriminator evaluates the authenticity of these sequences against real ones. This interaction between both models allows the generator to continuously improve its ability to produce data that is indistinguishable from real data. RNN GANs are relevant in applications where temporality and sequentiality are crucial, such as in text generation, music creation, or time series forecasting. Their ability to learn complex patterns in sequential data makes them a powerful tool in the field of machine learning and artificial intelligence, opening new possibilities in content creation and data simulation.
