RNN

[!WARNING] This is a work in progress and may contain inaccuracies or incomplete information. Introduction RNNs are first working initiative to process sequential data by maintaining a hidden state that captures information about previous elements (some early varient of context) in the sequence. They are widely used in various applications such as language modeling, speech recognition, and time series prediction. Different types of sequential data include: Text data (e.g., sentences, documents) Time series data (e.g., stock prices, weather data) Audio data (e.g., speech signals, music) Video data (e.g., frames in a video sequence) Biological sequences (e.g., DNA, protein sequences) sequence of actions (e.g., user behavior, robot movements) Different ways to implement RNNs include: ...