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Learning

Overview#

Learning is the activity of gaining knowledge or skill by studying, practicing, being taught, or experiencing something.

Learning enhances the awareness of the subjects of the study.

The ability of Learning is possessed by humans, some animals, and Artificial Intelligence-enabled systems. Learning is categorized as follows:

Auditory Learning#

Auditory Learning is Learning by listening and hearing. For example, students listening to recorded audio lectures.

Episodic Learning or Inductive Learning#

Episodic Learning is Learning by remembering sequences of events that one has witnessed or experienced. This is linear and orderly.

Motor Learning#

Motor Learning is learning by precise movement of muscles. For example, picking objects, Writing, etc.

Observational Learning #

Observational Learning is by watching and imitating others. For example, child tries to learn by mimicking her parent.

Perceptual Learning#

Perceptual Learning is learning through perception skills such as differentiating two musical tones from one another or categorizations of spatial and temporal patterns relevant to real-world expertise as in reading, seeing relations among chess pieces, knowing whether or not an X-ray image shows a tumor.

Relational Learning #

Relational Learning involves learning to differentiate among various stimuli on the basis of relational properties, rather than absolute properties. For Example, Adding "little less" salt at the time of cooking potatoes that came up salty last time, when cooked with adding say a tablespoon of salt.

Spatial Learning#

Spatial Learning is learning through visual stimuli such as images, colors, maps, etc. For Example, A person can create roadmap in mind before actually following the road.

Stimulus-Response Learning#

Stimulus-Response Learning is learning to perform a particular behavior when a certain stimulus is present. For example, a dog raises its ear on hearing doorbell.

Supervised Learning #

Supervised Learning is the Machine Learning

Deep Learning#

Deep Learning architectures such as deep Artificial Neural networks, deep belief networks and Recurrent Neural networks have been applied to fields including computer vision, speech recognition, Natural Language Processing, sound recognition, social network filtering, machine translation, bioinformatics and drug design, where they have produced results comparable to and in some cases superior to human experts

More Information#

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