Introduction to Artificial Neural Networks (ANN)
Artificial Neural Networks (ANN) or neural networks are computational algorithms that intend to simulate the behavior of biological systems composed of neurons.
ANNs are computational models inspired by an animal's central nervous system.
It is capable of machine learning techniques as well as pattern recognition.
A neural network is an oriented graph. It consists of nodes which in the biological analogy represent neurons, connected by arcs.
It corresponds to dendrites and synapses. Each arc is associated with a weight at each node.
A neural network is a machine learning algorithm based on the model of a human neuron. The human brain consists of millions of neurons.
It sends and processes signals in the form of electrical and chemical signals.
These neurons are connected with a special structure known as synapses. Synapses allow neurons to pass signals.
An Artificial Neural Network is an information processing technique. It works like the way the human brain processes information.
ANN includes a large number of connected processing units that work together to process information. They also generate meaningful information from it.
A neural network contains the following three layers:
- Input Layer: The activity of input units represents the raw information that can feed into the network.
- Hidden Layer:
- Hidden Layer is used to determine the activity of each hidden unit.
- The activities of the input units and the weights depend on the connections between the input and the hidden units.
- There may be one or more hidden layers.
- Output Layer: The behavior of the output units depends on the activity of the hidden units and the weights between the hidden and output units.