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Working of a Neural Network

A neural network is able to produce better results with larger amount of input data as compared to traditional
machine learning algorithms. A neural network is the complex structure in AI systems which is made up of
artificial neurons. This network is able to take multiple inputs to produce a single and meaningful output. In
general, a neural network consists of an input and output layer with one or multiple hidden layers within.

The basic architecture of a neural network is shown in the figure given below:
Input layer Hidden layer Output layer

Input # 1

Input # 2

Output
Input # 3

Input # 4

Here, input layer nodes receive only one value or input at one time and send it to all the nodes available in
the hidden layer. No processing takes place at the input layer due to its passive nature. The hidden layer nodes
perform specific functions on the incoming data and pass the processed information to the output layer node.

Each node available in the hidden layer acts like a separate machine learning algorithm. The execution of the
algorithm starts when it receives data from the input layer. After that, processed information is then passed
to the subsequent hidden layer, if available. The number of hidden layers depends upon the complexity of a
function for which it was configured.

In the last stage, the last hidden layer passes the processed information to the node of the output layer. Similar
to the input layer, the output layer cannot make any modification in the acquired information.

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There can be multiple hidden layers in a neural network and their number depends upon the complexity
of the function for which the network has been confi gured. Also, the number of nodes in each layer can
vary according to the complexity and the purpose of the task involved.

Applications of Neural Network

The concept of neural network has been widely used in pattern recognition,
facial recognition, customer support chatbot, vegetable price prediction etc.
Some of the important applications of neural networks are:
u Product Recommendation Engine: Product Recommendation Engine is
a field where image classification and object recognition can be easily
carried out by ANNs. For example, Amazon uses ANN image recognition
for suggestions in the “You might also like” section. The basis of the
assumption is the user’s expressed behaviour. The products themselves
are matched on visual criteria like black high heels for a black dress.

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