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.
Comments
Post a Comment