incremental-learning

Incremental learning allows models to learn from new data in a step-wise manner, which is particularly useful in environments where data is continually generated. This approach contrasts with traditional batch learning, where models must be retrained from the beginning each time new data becomes available. A common misconception is that incremental learning is only suitable for small datasets; in reality, it can handle large datasets effectively by focusing on new information without losing previously learned knowledge.