Implementation of Basic Neural Network Model
A neural network is a model characterized by an activation
function which is used by interconnected information processing units to
transform input into output.
The first layer of
the neural network receives the raw input process, it processes it and passes
the processed information to the hidden layer.
The hidden layer passes the information to the last layer which
produces the output.
The advantage of neural network is that it is adaptive in
nature , it learns from the information provided, it trains itself from the
data which has a known outcome and optimizes its weight for a better prediction
in situations with unknown outcomes.
A perception single layer neural network is the most basic
neural network model. A perception receives multidimensional input and
processes it using a weight summation and an activation function.
It is trained using a labeled data and learning algorithm that optimizes the
weight in the summation processor.
A major limitation of perception model is its inability to deal
with non-linearity.
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