Implementation of Basic Neural Network Model


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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