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➡️ Machine Learning from Scratch, part 37: softmax. Every neuron first adds up its inputs times their weights, then an activation function reshapes the result. Without activation functions, any number of layers would act like one single linear layer. Softmax is the special activation at the very end of a network that sorts things into classes. The last layer has one output neuron per class, and each one gives a raw score that can be any number, even negative. Softmax looks at all of them at once
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