Real Close to the Machine: Floating Point in D | Using Floating Point Without Losing Your Sanity by Dan Clugston [outside article & video]


An entertaining look at floating point computation and absurdities that you might encounter if you are not careful. The video is entertaining; the details are in the article.

Computers were originally conceived as devices for performing mathematics. The earliest computers spent most of their time solving equations. Although the engineering and scientific community now forms only a miniscule part of the computing world, there is a fantastic legacy from those former times: almost all computers now feature superb hardware for performing mathematical calculations accurately and extremely quickly. Sadly, most programming languages make it difficult for programmers to take full advantage of this hardware. An even bigger problem is the lack of documentation; even for many mathematical programmers, aspects of floating-point arithmetic remain shrouded in mystery.



Missing values in Julia by Milan Bouchet-Valat [outside article]


Starting from Julia 0.7, missing values are represented using the new missing object. Resulting from intense design discussions, experimentations and language improvements developed over several years, it is the heir of the NA value implemented in the DataArrays package, which used to be the standard way of representing missing data in Julia. Continue reading…

Gradient Descent: The mother of all algorithms? by Aleksander Mądry [outside lecture]


More than half a century of research in theoretical computer science has brought us a great wealth of advanced algorithmic techniques. These techniques can be combined in a variety of ways to provide us with sophisticated, often beautifully elegant algorithms. This diversity of methods is truly stimulating and intellectually satisfying. But is it also necessary? Continue reading…

Artificial Intelligence — the revolution hasn’t happened yet by Michael Jordan [outside article]


A thoughtful article by one of the leading machine learning researchers on whether we can call “machine learning” “artificial intelligence”.

Artificial Intelligence (AI) is the mantra of the current era. The phrase is intoned by technologists, academicians, journalists and venture capitalists alike. As with many phrases that cross over from technical academic fields into general circulation, there is significant misunderstanding accompanying the use of the phrase. But this is not the classical case of the public not understanding the scientists — here the scientists are often as befuddled as the public. The idea that our era is somehow seeing the emergence of an intelligence in silicon that rivals our own entertains all of us — enthralling us and frightening us in equal measure. And, unfortunately, it distracts us.

Deep Learning by Ian Goodfellow and Yoshua Bengio and Aaron Courville [book]


The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular. The complete version of the book including lecture materials is available online for free.