Deep Learning: Old Wine, New Labels
May 13, 2016
I read “Deep Learning: Definition, Resources, and Comparison with Machine Learning.” The most useful segment of the article to me is the list of resources. I did highlight this statement and its links:
Many deep learning algorithms (clustering, pattern recognition, automated bidding, recommendation engine, and so on) — even though they appear in new contexts such as IoT or machine to machine communication — still rely on relatively old-fashioned techniques such as logistic regression, SVM, decision trees, K-NN, naive Bayes, Bayesian modeling, ensembles, random forests, signal processing, filtering, graph theory, gaming theory, and many others. Click here and here for details about the top 10 algorithms.
The point is that folks are getting interested in established methods hooked together in interesting ways. Perhaps new methods will find their way into the high flying vehicles for smart software? But wait. Are computational barriers acting like a venturi in the innovation flow? What about that vacuum?
Stephen E Arnold, May 13, 2016
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