Sequential Modeling
Sequential modeling is a machine learning approach that processes data sequences, such as time series, text, or audio, by capturing dependencies between elements in order. It involves models that maintain a state or memory of previous inputs to predict or generate subsequent outputs, making it essential for tasks where context and order matter. Common applications include natural language processing, speech recognition, and financial forecasting.
Developers should learn sequential modeling when working with data that has inherent temporal or sequential structure, such as predicting stock prices, translating languages, or generating text. It is crucial for building systems that require understanding of context over time, like chatbots, recommendation engines, or anomaly detection in sensor data. Mastery of this concept enables effective use of specialized models like RNNs, LSTMs, and Transformers in real-world scenarios.