Activation functions are really important for a Neural Network to learn and make sense of something really complicated and Non-linear complex functional mappings between the inputs and response variable.They introduce non-linear properties to our Network.Their main purpose is to convert a input signal of a node in a A-NN to an output signal. That output signal now is used as a input in the next layer in the stack. Specifically in A-NN we do the sum of products of inputs(X) and their corresponding Weights(W) and apply a Activation function f(x) to it to get the output of that layer and feed it as an input to the next layer. In keras, we can use different activation function for each layer. That means that in our case we have to decide what activation function we should be utilized in the hidden layer and the output layer. Activations can either be used through an Activation layer, or through the activation argument supported by all forward layers: “Input times weights
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