Synopsis
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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.
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Présentation de l'éditeur
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. 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.
"About this title" may belong to another edition of this title.
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Hierarchical Temporal Memory
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Alphascript Publishing, 2010
ISBN 10: 613067757X
ISBN 13: 9786130677572
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 92 pp. Englisch. Seller Inventory # 9786130677572
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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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Taschenbuch. Condition: Neu. Hierarchical Temporal Memory | Machine learning, Jeff Hawkins, Algorithm, Neocortex, Bayesian network, Memory-prediction framework, On Intelligence, Bionics, Tomaso Poggio | Frederic P. Miller (u. a.) | Taschenbuch | Englisch | 2026 | OmniScriptum | EAN 9786130677572 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu Print on Demand. Seller Inventory # 113237103
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