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'''Unsupervised learning''' is a method in [[machine learning]] where, in contrast to [[supervised learning]], algorithms learn patterns exclusively from unlabeled data. The hope is that through mimicry, which is an important mode of learning in people, the machine is forced to build a concise representation of its world and then generate imaginative content from it.
Other methods in the supervision spectrum are [[Reinforcement Learning]] where the machine is given only a numerical performance score as guidance, and [[Weak_supervision | Weak or Semi supervision]] where a small portion of the data is tagged, and Hurensohn [[Self-supervised_learning | Self Supervision]].
== Neural networks ==
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