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are connected by links, and they interact with each other. The nodes can take input data and perform
simple operations on the data. The result of these operations is passed to other neurons. The output at
each node is called its activation or node value. Each link is associated with weight. ANNs are capable of
learning, which takes place by altering weight values.

Simple Artificial Neural Network Architecture
A basic neural network has interconnected artificial neurons in three layers:
1. Input Layer: Information from the outside world enters the artificial
neural network from the input layer. Input nodes process the data,

analyse or categorise it, and pass it on to the next layer.
2. Hidden Layer: Hidden layers take their input from the input layer
or other hidden layers. Artificial neural networks can have a large
number of hidden layers. Each hidden layer analyses the output from
the previous layer, processes it further, and passes it on to the next
layer.
3. Output Layer: The output layer gives the final result of all the data processing by the artificial
neural network. It can have single or multiple nodes. For instance, if we have a binary (Yes/No)

classification problem, the output layer will have one output node, which will give the result as
1 or 0. However, if we have a multi-class classification problem, the output layer might consist of
more than one output node.

Types of Artificial Neural Networks
Based on the way they operate, Artificial Neural Networks can be of several types:
1. Feed-Forward Neural Network
Feed-forward neural network is the simplest ANN. It conveys
information in one direction through input nodes. The

information continues to be processed in this single direction
until it reaches the output mode. Feed-forward neural networks
may have one or more hidden layers for functionality. This type
of ANN is most often used for facial recognition technologies.
2. Recurrent Neural Network
A more complex type of neural network, the

Recurrent neural network takes the output of a
processing node and transmits the information back
into the network. This results in theoretical learning
and improvement of the network. Each node stores
historical processes, and these historical processes
are reused in the future during processing. This
technique is especially critical for networks in which
the prediction is incorrect; the system will attempt to learn why the correct outcome occurred and

adjust accordingly. This type of neural network is often used in text-to-speech applications.

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