Recurrent neural networks (RNNs) are a type of neural network that are well-suited for sequence-based problems, such as speech recognition, weather forecasting, and time series analysis. In RNNs, the next state of the system depends on the previous state, allowing them to capture temporal dependencies in data. This is illustrated through diagrams and worked examples, showing how RNNs can be used to model and predict sequential data. The article also provides insights into the architecture and functioning of RNNs, highlighting their ability to handle sequential data effectively. Overall, RNNs are a powerful tool for tasks that involve sequences, making them a valuable tool in various fields such as natural language processing, time series analysis, and speech recognition.
Source link
Source link: https://towardsdatascience.com/recurrent-neural-networks-an-introduction-to-sequence-modelling-478e0e07c4ec
#Introduction to Sequence Modelling with Recurrent Neural Networks #RNNs
