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The Softmax activation function is widely used in machine learning, particularly for classification tasks. It normalizes raw model outputs, called logits, into probabilities. Each output value is scaled to fall between 0 and 1, with the sum of all outputs equaling 1. This makes Softmax ideal for multi-class classification problems, as it enables the model to predict the likelihood of each class. The function emphasizes the most confident predictions while suppressing the less probable ones, helping the model make clear and interpretable decisions in classification scenarios.

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