👉 A mapping fluid, often used in the context of neural networks and machine learning, is a type of activation function that maps input data to a range that reflects the semantic or structural relationships within the data. Unlike standard activation functions such as ReLU or sigmoid, which can produce outputs that are either zero or in a limited range, mapping fluids adaptively adjust their output based on the input's context. This allows them to capture complex, non-linear relationships and dependencies, making them particularly useful in tasks like natural language processing and image recognition where understanding the nuanced relationships between elements is crucial. By mapping inputs to a more meaningful or structured space, these fluids enhance the model's ability to generalize and make accurate predictions.
