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.
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?
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… Read More
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. http://www.deeplearningbook.org/
A presentation on Deep Learning including a brief history and tutorial. https://www.dropbox.com/s/qonozmne0x4x2r3/deepsurveyICML18final.pptx
