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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