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Real-Time Machine Learning with Node.js by Philipp Burckhardt, Carnegie Mellon University



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Real-Time Machine Learning with Node.js – Philipp Burckhardt, Carnegie Mellon University

Real-time machine learning provides statistical methods to obtain actionable, immediate insights in settings where data becomes available in sequential order. After providing an overview of state of the art real-time machine learning algorithms, we discuss how these algorithms can be leveraged from within a Node.js application. We will see why the powerful API of the core stream module makes Node.js a more attractive platform for such tasks compared to languages traditionally used for scientific computing such as R, Python or Julia. Finally, we will discuss best-practices and common pitfalls that one faces when using these algorithms.

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9 thoughts on “Real-Time Machine Learning with Node.js by Philipp Burckhardt, Carnegie Mellon University
  1. alot of computation on a single threaded system ??? just wait for web assembly spec to be finalized and node will be the first to support it then u just include your C++ model and get the best of the two worlds

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