Difference between revisions of "Hierarchical Temporal Memory (HTM)"
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* [[Anomaly Detection]] | * [[Anomaly Detection]] | ||
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| + | HTM are learning algorithms that can store, learn, infer, and recall high-order sequences. Unlike most other machine learning methods, HTM continuously learns (in an unsupervised process) time-based patterns in unlabeled data. HTM is robust to noise, and has high capacity (it can learn multiple patterns simultaneously). [Wikipedia] | ||
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| + | http://upload.wikimedia.org/wikipedia/commons/0/05/Neuron_comparison.png | ||
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<youtube>6ufPpZDmPKA</youtube> | <youtube>6ufPpZDmPKA</youtube> | ||
Revision as of 11:25, 9 August 2020
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- Temporal Computing
- Hierarchical Clustering; Agglomerative (HAC) & Divisive (HDC)
- Hierarchical Cluster Analysis (HCA)
- Capabilities
- Clustering
- An Alternative to Deep Learning? Guide to Hierarchical Temporal Memory (HTM) for Unsupervised Learning | Tavish Srivastava
- Numenta HTM School
- Hierarchical Temporal Memory for Real-time Anomaly Detection | Ihor Bobak
- Numentra - Where Neuroscience Meets Machine Intelligence
- Hierarchical Temporal Memory (HTM) School | Matt Taylor - Numentra
- Numenta.org
- Biological and Machine Intelligence (BAMI)
- Anomaly Detection
HTM are learning algorithms that can store, learn, infer, and recall high-order sequences. Unlike most other machine learning methods, HTM continuously learns (in an unsupervised process) time-based patterns in unlabeled data. HTM is robust to noise, and has high capacity (it can learn multiple patterns simultaneously). [Wikipedia]
Jeff Hawkins - Study of the Brain and Development of Intelligent Machines
