Softmax Function
Softmax Function is an activation function that converts a set of numerical values into probabilities that sum to 1, commonly used to represent the predicted probabilities of different classes in a classification model.
What is Softmax Function?
The Softmax function takes the output scores, or logits, from a neural network and converts them into values between 0 and 1. Each value represents the model's estimated probability for a particular class. The probabilities across all classes sum to 1, making Softmax especially useful for multi-class classification.
Why is Softmax Function Important?
Softmax makes raw model outputs easier to interpret as probabilities and helps classification systems determine which class is most likely. It is commonly used in the final layer of neural networks for multi-class classification tasks.
Common use cases
Softmax Function is commonly used in image classification, natural language processing, speech recognition, recommendation systems, and other multi-class machine learning applications.