Recurrent Neural Network

Recurrent Neural Networks (RNNs) are a type of neural network used for processing sequential data such as time-series or text. RNNs use feedback loops to maintain an internal state that depends on previous inputs. The architecture consists of a set of nodes that are connected to each other in a chain-like structure. The vanishing gradient problem is a challenge in training RNNs. Techniques such as LSTM and GRU networks have been developed to address this issue. RNNs have been applied to a wide range of applications and are an active area of research. They are particularly useful for tasks such as speech recognition, language translation, and handwriting recognition.

• Introduction to Recurrent Neural Networks
• Some applications of RNNs in machine learning
• Recurrent Neural Networks (RNNs), Clearly Explained
• Illustrated Guide to Recurrent Neural Networks: Understanding the Intuition
• Simple Recurrent Neural Network with Keras