Python for Deep Learning - Build Neural Networks in Python - SoftMax Function

The video tutorial explains the softmax function, which operates in the output layer of neural networks. It applies an exponential function to the output values and normalizes them so that they sum to 1. This process helps compute probabilities or confidence scores for each attribute, making it useful in multiclass classification problems. Additionally, the softmax function is differentiable, which is important for training neural networks.

In what type of problems is the softmax function used?

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Why is the softmax function considered a differentiable function?

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