Neural Work

A neural network is a system of hardware and/or software patterned same as the operation of neurons in the human brain. Neural networks are also called artificial neural networks. It is a type of deep learning technologies.

A neural network usually includes a large number of processors operating in parallel and which are arranged in tiers. The raw input information is received by the first tier -- analogous to optic nerves in human visual processing. Every next tier gets the output from its preceding tier, rather than from the raw input. In the same way, neurons distant from the optic nerve receive signals from the neurons which are closer to it. The output of the system is produced by the last tier.

Artificial Neural Networks

Artificial neural networks (ANNs) systems are the computing systems which are inspired by the biological neural networks that represent animal brains. Such systems learn tasks by considering examples, usually without task-specific programming. For example, image recognition. ANN can learn to spot images that contain dogs by analyzing example images that have been manually labelled as "dog" or "no dog" and using the results to spot dogs in other images.

An ANN is based on the collection of connected units or nodes called artificial neurons (analogous to biological neurons in an animal brain). Every connection (analogous to a synapse) between the artificial neurons transmits a signal to one another. The artificial neuron which receives the signal processes it and then signals artificial neurons connected to it.

At first, the main goal of the ANN approach was to solve the problems in the way a human brain would solve it. But over time, the attention focused on matching specific mental abilities, leading to deviations from biology. It have been used in different tasks and fields, such as computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis.

Neural network have a great ability to extract data with proper meaning from any complex or imprecise data. This ability of Neural Network is used to extract patterns and detect trends which are too complicated to be noticed by either humans or other computer techniques. A trained neural network can be supposed as an "expert" in the category of information it has been given to analyze. This expert later is used to provide projections given new situations of interest and answer the "what if" questions to any problem.

Other advantage of Neural Network includes:

• Adaptive learning: An ability to find out the way to do tasks based on the information given for training or initial experience.
• Self-Organisation: ANN can create its own representation or organisation of the information which it will receive during the learning time
• Real-Time Operation: ANN computations can execute in parallel. Some special hardware devices are also being designed and manufactured which will take the advantage of this capability of ANN.
• Fault Tolerance via Redundant Information Coding: The performance will effect due to the partial destruction of network. However, some network capabilities can be preserved even with major network damage.