Thu, Jul 06, 2017 • Events

SF Big Analytics Meetup: Push the Limits of Kafka & Streaming Analytics with Hierarchal Temporal Memory

Yuwei Cui • Research Engineer

Abstract:

Much of the world’s data is becoming streaming, time-series data. It becomes
increasingly important to analyze streaming data in real-time. Hierarchical
Temporal Memory (HTM) is a detailed computational theory of the neocortex. At
the core of HTM are time-based learning algorithms that store and recall spatial
and temporal patterns. HTM is well suited to a wide variety of problems;
particularly those involve streaming data and time-based patterns. The current
HTM systems are able to learn the structure of streaming data, make predictions
and detect anomalies. It is distinguished from other techniques in its ability
to learn continuously in a fully unsupervised manner. HTM has been tested and
implemented in software, all of which is developed with best practices and is
suitable for deploying in commercial applications. The core learning algorithms
are fully documented and available in an open source project called NuPIC. HTM
not only advances our understanding of how the brain may solve the sequence
learning problem but is also applicable to real-world sequence learning problems
from continuous data streams.

Yuwei Cui • Research Engineer

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