Artificial Neural Network
Artificial Neural Network
An Artificial Neural Network (ANN) is a computational model based on the structure and functions of biological Neural Networks. They are used to estimate or approximate functions that can depend on a large number of inputs and are generally unknown. Artificial Neural Networks are generally presented as systems of interconnected "neurons" which can compute values from inputs.
Pronunciation
Artificial Neural Network: /ɑːr.tɪˈfɪʃ.əl ˈnʊə.rəl ˈnɛt.wɜːk/
Etymology
The term "Artificial Neural Network" is derived from the natural biological neural network that is a part of the Human Brain. The word "Artificial" is used to differentiate the model from natural neural networks.
Related Terms
- Neuron: A neuron is a nerve cell that is the basic building block of the nervous system. Neurons are similar to other cells in the human body in a number of ways, but there is one key difference between neurons and other cells. Neurons are specialized to transmit information throughout the body.
- Machine Learning: Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
- Deep Learning: Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost.
See Also
This WikiMD.org article is a stub. You can help make it a full article.