Introduction to artificial neural network
The term "Artificial neural network" indicates a biologically inspired sub-field of artificial intelligence modelled after the brain. An artificial neural network is a computational network based on biological neural networks that construct the structure of the human brain.
Similar to a human brain that has neurons interconnected to one other, artificial neural networks also have neurons that are associated with each other in various layers of the networks. These neurons are known as nodes.
Understanding Artificial Neural Network
An Artificial Neural Network in the domain of Artificial intelligence where it tries to mimic the network of neurons that makes up a human brain so that computers will have a choice to learn things and make a decision in a human-like manner.
Programming computers design the artificial neural network to behave like interconnected brain cells.
There are almost 1000 billion neurons in the human brain. Each neuron has an association point in the range of 1,000 and 100,000.
In the human brain, the data is stored in a way as to be distributed, and we can extract more than a single piece of this data when it is needed from our memory parallelly.
Architecture Of An Artificial Neural Network
Artificial Neural Network fundamentally consists of three layers: - Input Layer - Hidden Layer - Output Layer
Advantages Of Artificial Neural Network
Parallel processing capability-Artificial neural networks hold a numerical value that can perform more than one task together.
Storing data on the whole network -Data utilized in traditional programming is stored on the entire network, not on a database. The removal of a couple of pieces of data in one place doesn't stop the network from working.
Ability to work with incomplete knowledge -After ANN training, the information can produce output even with sparse data. The loss of performance here relies on the significance of missing data.
Having a memory distribution -For ANN is to be able to adapt, it is essential to ascertain the examples and to encourage the network according to the desired output by demonstrating the examples to the network. The succession of the network is known to be directly proportional to the chosen instances, and if the event cannot appear to the network in all its aspects, it can produce false output.
Having fault tolerance-Extortion of one or more cells of ANN does not prevent it from producing output, and this feature makes the network fault-tolerance.
