Implementation of neural network package


Implementation  of  neural network package

A neural network is a computational system frequently employed in machine learning to create predictions based on existing data.

A typical neural network consists of –
Input Layers : Layers that take input based on existing data.

Hidden Layers: Layers that use back propagation to optimize the weights of the input variables in order to improve the predictive power  of the model.

Output Layer : Output of prediction based on the data from the input and hidden layers.

Neural networks have received a lot of attention for their abilities to learn relationships among variables.

They represent an innovative technique for model fitting that doesn’t exist on conventional assumption necessary for standard models and they can also quite effectively handle multivariable response data . They are very similar to non-linear regression model with exception that the former can handle an incredible large amount of model parameter.




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