The softmax function and sigmoid function are both activation functions used in neural networks, but they serve different purposes and have distinct characteristics.
Sigmoid Function:
Formula: ( sigma(x) = frac{1}{1 + e^{-x}} )
Range: Outputs values between 0 and 1.
Usage: Commonly used in binary classification problems where the output represents the probability of a single class.
Characteristics: The sigmoid function squashes input values to a range between 0 and 1, making it useful for probabilistic interpretations. However, it can suffer from vanishing gradients, which slow down
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