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function controls the scale of the output. For example, an acceptable range of output is usually
between 0 and 1, or it could be −1 and 1.
How do Neural Networks Work?
The human brain is the inspiration behind artificial neural network architecture. Human brain cells,
called neurons, form a complex, highly interconnected network and send electrical signals to each
other to help humans process information. Similarly, an artificial neural network is made of artificial
neurons that work together to solve a problem. Artificial neurons are software modules called nodes,
and artificial neural networks are software programs or algorithms that use computing systems to
solve mathematical calculations.
ANNs are composed of multiple nodes which imitate biological neurons of human brain. 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. 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.
Types of Artificial Neural Networks
Based on the way they operate, Artificial Neural Networks can
be of several types:
1. Feed-Forward Neural Network
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. This type of ANN is most often
used for facial recognition technologies.
2. Recurrent 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. This technique is
especially critical for networks in which the prediction
is incorrect; the system will attempt to learn why the
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