Editing Unsupervised learning (section)

== Tasks ==
[[File:Task-guidance.png|thumb|left|300px|Tendency for a task to employ supervised vs. unsupervised methods. Task names straddling circle boundaries is intentional. It shows that the classical division of imaginative tasks (left) employing unsupervised methods is blurred in today's learning schemes.]]Tasks are often categorized as [[Discriminative model|discriminative]] (recognition) or [[Generative model|generative]] (imagination).  Often but not always, discriminative tasks use supervised methods and generative tasks use unsupervised (see [[Venn diagram]]); however, the separation is very hazy.  For example, object recognition favors supervised learning but unsupervised learning can also cluster objects into groups.  Furthermore, as progress marches onward, some tasks employ both methods, and some tasks swing from one to another.  For example, image recognition started off as heavily supervised, but became hybrid by employing unsupervised pre-training, and then moved towards supervision again with the advent of [[Dilution (neural networks)|dropout]], [[Rectifier (neural networks)|ReLU]], and [[Learning rate|adaptive learning rates]].

A typical generative task is as follows. At each step, a datapoint is sampled from the dataset, and part of the data is removed, and the model must infer the removed part. This is particularly clear for the [[Autoencoder|denoising autoencoders]] and [[BERT (language model)|BERT]].