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Hidden layer(s) are the secret sauce of your network. They allow you to model complex data thanks to their nodes/neurons. They are “hidden” because the true values of their nodes are unknown in the training dataset. In fact, we only know the input and output. Each neural network has at least one hidden layer.
Hidden layers, simply put, are layers of mathematical functions each designed to produce an output specific to an intended result. Hidden layers allow for the function of a neural network to be broken down into specific transformations of the data. Each hidden layer function is specialized to produce a defined output.
In neural networks, a hidden layer is located between the input and output of the algorithm , in which the function applies weights to the inputs and directs them through an activation function as the output. In short, the hidden layers perform nonlinear transformations of the inputs entered into the network.
What is a ‘layer’ in a neural network?
Layer is a general term that applies to a collection of ‘nodes’ operating together at a specific depth within a neural network. The input layer is contains your raw data (you can think of each variable as a ‘node’). The hidden layer(s) are where the black magic happens in neural networks.
What does a neural network actually do?
we have an input layer of source nodes projected on an output layer of neurons. This network is a feedforward or acyclic network.
Is there neural network that has two input layers?
The basic neural network only has two layers the input layer and the output layer and no hidden layer. In that case, the output layer is the price of the house that we have to predict.