📢 Note : All hidden layers usually use the same activation function. It’s the final layer of the network that brings the information learned through the hidden layer and delivers the final value as a result. The hidden layer performs all kinds of computation on the features entered through the input layer and transfers the result to the output layer.
They provide an abstraction to the neural network. Hidden LayerĪs the name suggests, the nodes of this layer are not exposed.
Nodes here just pass on the information (features) to the hidden layer. No computation is performed at this layer. The input layer takes raw input from the domain. Each of them is characterized by its weight, bias, and activation function. In the image above, you can see a neural network made of interconnected neurons.
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