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 :
1.
Input Layers : Layers that take input based on existing
data.
2.
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.
3.
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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