A neural network is a system of hardware and/or software modeled on the same functioning as neurons in the human brain. Neural networks are also called artificial neural networks. It is a type of deep learning technology. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an Original Essay A neural network usually includes a large number of processors operating in parallel and which are arranged in layers. Incoming raw information is received by the first layer, analogous to optic nerves in human visual processing. Each subsequent layer gets the output from the previous layer, rather than the raw input. Similarly, neurons distant from the optic nerve receive signals from neurons closer to it. The output of the system is produced by the last level. Artificial Neural Networks Artificial neural network (ANN) systems are computer systems inspired by biological neural networks that represent animal brains. Such systems learn tasks by considering examples, usually without task-specific programming. For example, image recognition. An ANN can learn to spot images that contain dogs by analyzing sample images that have been manually labeled 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's brain). Each connection (analogous to a synapse) between artificial neurons transmits a signal to each other. The artificial neuron that receives the signal processes it and then signals to the artificial neurons connected to it. Initially, the main goal of the ANN approach was to solve problems the way a human brain would solve them. But over time, attention has focused on matching specific mental abilities, leading to deviations from biology. They have been used in diverse tasks and fields, such as computer vision, speech recognition, machine translation, social network filtering, board and video games, and medical diagnosis. The neural network has a great ability to extract data with the correct meaning from any complex or imprecise data. This neural network capability is used to extract patterns and detect trends that are too complicated for humans or other computing techniques to notice. A trained neural network can be considered an "expert" in the category of information it needs to analyze. This expert is subsequently used to provide projections given new situations of interest and answer “what if” questions for any problem. Please note: this is just an example. Get a custom paper from our expert writers now. Get a Custom Essay Other benefit of Neural Network includes: Adaptive Learning: Ability to discover how to perform tasks based on the information provided during training or initial experience. Self-organization: The ANN can create its own representation or organization of the information it will receive during learning timeReal-time operation: ANN calculations can be performed in parallel. Some special hardware devices are also being designed and manufactured that will take advantage of this ANN capability. Fault tolerance by coding redundant information: Performance will be affected due to partial destruction of the network. However, some network functionality can be preserved even in the event of severe network damage.