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Hierarchical Temporal Memory: Machine learning, Jeff Hawkins, Algorithm, Neocortex, Bayesian network, Memory-prediction framework, On Intelligence, Bionics, Tomaso Poggio -

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2026-03-15
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Hierarchical temporal memory is a machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex using an approach somewhat similar to Bayesian networks. HTM model is based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTMs are claimed to be biomimeti ... Pilns apraksts

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Hierarchical temporal memory is a machine learning model developed by Jeff Hawkins and Dileep George of Numenta, Inc. that models some of the structural and algorithmic properties of the neocortex using an approach somewhat similar to Bayesian networks. HTM model is based on the memory-prediction theory of brain function described by Jeff Hawkins in his book On Intelligence. HTMs are claimed to be biomimetic models of cause inference in intelligence. Jeff Hawkins states that HTM does not present any new idea or theory, but combines existing ideas to mimic the neocortex with the simplest design that provides the greatest range of capabilities. He stated this is similar to the Palm Pilot, a device he designed that became popular because of its particular blend of old features. Similarities to existing AI ideas are described in the December 2005 issue of the Artificial Intelligence journal. It is similar to work by Tomaso Poggio and David Mumford.

Vairāk informācijas

Izdevējs OmniScriptum
Izlaides gads 2026
Vāka tips Mīkstais vāks
EAN 9786130677572
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Jūs vērtējat: Hierarchical Temporal Memory: Machine learning, Jeff Hawkins, Algorithm, Neocortex, Bayesian network, Memory-prediction framework, On Intelligence, Bionics, Tomaso Poggio
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137,01 € 195,73 €