Roundup 11/3/2016

Machine learning (ML) and deep learning (DL) content from the past 24 hours. Plus, some AI stuff.

The Next Platform publishes my three-part series on the state of enterprise machine learning. Part one is here. Part two is here. Part three is here.

Good Reads

— On the BigML blog, Charles Parker applies the wisdom of E. W. Dijkstra to machine learning, in two parts. Part one. Part two.

— In Forbes, Adrian Bridgewater asks whether Microsoft can decode the future of artificial intelligence. I’m guessing that it can, but will first make you upgrade to Windows 10.

Fundamentals

— Ian Barker explains deep learning.

— Jason Brownlee contemplates randomness as if he discovered it, or something.

Methods and Techniques

— Jean-Francois Puget argues that ML is actually mathematical optimization, which seems accurate. What’s interesting is that there are so few optimization algorithms in use: stochastic gradient descent, conjugate gradient, L-BFGS and a few others. Master optimization and you master ML.

— Joseph Chen asks how Lean Six Sigma can help machine learning. It certainly can’t hurt.

— Researchers at MIT’s CSAIL lab develop a technique to generate text fragments that justify predictions by neural nets. (Trigger warning: there is math.) In SD Times, Christina Cardoza reports.

— Joe Rickert explains the Naive Bayes classifier.

Software and Services

— Alex Ioannides touts the future package, an R package that supports asynchronous and distributed computing for embarrassingly parallel operations.

Applications

— Researchers at Princeton and the University of California use ML to identify suspicious domain names.

— John Pitcher discusses the potential for ML to control building systems, such as HVAC.

— Symantec introduces a new release of Endpoint Protection, which uses ML to defeat cyber threats.

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