What are vanilla neural networks

This recipe explains what are vanilla neural networks

Recipe Objective - What are vanilla neural network?.

Vanilla neural networks are termed as an extension to linear regression supervised algorithm. Vanilla neural networks are similar to other linear regression and just a difference is a hidden layer that is an extra layer is added between the inputs and outputs which plays a major role as all the extra computations in vanilla neural networks works in the hidden layer. The hidden layer, denoted with H, has three “neurons” (H0, H1, H2) assuming and any number of neurons can be added in hidden layers. With hidden layer, backpropagation algorithm can be used in vanilla neural network.

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Explanation of Vanilla neural network.

Vanilla neural network make its architecture powerful so as to apply the nonlinear “activation function” to output of each layer involving weights and bias.

Vanilla neural network calculates weighted sum through each step depending upon number of layers in the network which depends upon the which type of activation function used that is the ReLU function, logit function or the hyperbolic tangent function(tanh) in the network.

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