Editing Neural network (machine learning) (section)

==== Supervised learning ====
[[Supervised learning]] uses a set of paired inputs and desired outputs. The learning task is to produce the desired output for each input. In this case, the cost function is related to eliminating incorrect deductions.<ref>{{Cite journal|last1=Ojha|first1=Varun Kumar|last2=Abraham|first2=Ajith|last3=Snášel|first3=Václav|date=1 April 2017|title=Metaheuristic design of feedforward neural networks: A review of two decades of research|journal=Engineering Applications of Artificial Intelligence|volume=60|pages=97–116|doi=10.1016/j.engappai.2017.01.013|arxiv=1705.05584|bibcode=2017arXiv170505584O|s2cid=27910748}}</ref> A commonly used cost is the [[mean-squared error]], which tries to minimize the average squared error between the network's output and the desired output. Tasks suited for supervised learning are [[pattern recognition]] (also known as classification) and [[Regression analysis|regression]] (also known as function approximation). Supervised learning is also applicable to sequential data (e.g., for handwriting, speech and [[gesture recognition]]). This can be thought of as learning with a "teacher", in the form of a function that provides continuous feedback on the quality of solutions obtained thus far.