Machine learning (ML) and deep learning (DL) content from the past 24 hours.
— McKinsey consultants Christoph Glatzel, Matt Hopkins, Tim Lange, and Uwe Weiss explain how retailers use machine learning to drive fresh food stocking.
— Unity Technologies hires Danny Lange, Uber’s head of machine learning.
— Vincent Granville revisits the central limit theorem.
— In an exclusive meeting, Apple reveals the state of its AI research: LiDAR, smaller neural networks and more. Apple promises to publish what it learns. No word about that headphone jack. Linkapalooza here.
— Steve Ranger reports on work by researchers from Cray, Microsoft, and the Swiss National Supercomputing Centre to speed up deep learning on supercomputers. The team has successfully used the Microsoft Cognitive Toolkit (CNTK) to train deep learning on a Cray XC50 (pictured below) with more than 1,000 NVIDIA Tesla P100 GPUs.
Methods and Techniques
— Mu Sigma’s Arpit Saxena asks: can weather data improve your predictive models? The answer must be yes, or the article wouldn’t amount to much. Saxena explains some things you should consider when you add weather data to a predictive model.
— Ben Frederickson offers an interactive tutorial on numerical optimization. It starts strong, but it’s all downhill from there. If you don’t get the joke, read the article.
— Qulix’ Aleksandr Sliborsky touts Azure Machine Learning in what appears to be a Microsoft astroturf blog. It’s still an interesting read.
— Inside HPC attends the Intel HPC Developer Conference and interviews a number of people on interesting topics: accelerating machine learning, anomaly detection, optimizing deep learning frameworks, distributed KNN, and other topics.
— Molly Olmstead explains how physicists use deep learning to identify subatomic particles.
— The BigML blog profiles contenders in the Brazilian AI Startup Battle.
Bottom Story of the Day
— IBM’s James Kobelius speculates about data science in 2017.