The ANN Components

There are three essential layers in a neural network:
  • Input layer: It receives the input information in the form of various texts, numbers, audio files, image pixels, etc.

  • Hidden layers: These hidden layers perform various types of mathematical computation on the input data and recognize the patterns that are part of.

  • Output layer: The output layer obtains the result that we obtain through rigorous computations performed by the middle layer.



There are multiple parameters that affect the performance of the ANNs, and the output of ANNs is mostly dependent on these parameters. Some of these parameters are weights, biases, learning rate, batch size, etc. Each node in the network has some weights assigned to it. Biases are values associated with each node in the input and hidden layers, but in practice are treated in exactly the same manner as other weights. The use of biases in a neural network increases the capacity of the network to solve problems.

A transfer function, ∑xi⋅wij as given below, is used for calculating the weighted sum of the inputs and the bias, which are fed to the activation function. 
   netj = x1⋅w1j + x2⋅w2j + x3⋅w3j + ...... + xn⋅wnj = ∑xi⋅wij



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