Different types of activation function

We now know that multi-layer networks can classify linearly inseparable classes. But to do this, they need to satisfy one more condition. If the neurons don't have activation functions, their output would be the weighted sum of the inputs, , which is a linear function. Then the entire neural network, that is, a composition of neurons, becomes a composition of linear functions, which is also a linear function. This means that even if we add hidden layers, the network will still be equivalent to a simple linear regression model, with all its limitations. To turn the network into a non-linear function, we'll ...

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