👉 The classification fluid, also known as the softmax function in neural networks, is a mathematical operation that converts raw output scores from a neural network into probabilities for each class. It takes the network's output, which is typically a vector of scores for various classes, and normalizes these scores so that they sum up to 1. This normalization ensures that each class's probability represents the likelihood of the input belonging to that class, making it a crucial component for tasks like classification where the model needs to predict one of several possible outcomes. The fluidity in its operation lies in its ability to adjust the probabilities dynamically based on the input data, providing a smooth and continuous transition between class probabilities.
