Machine learning (ML) and deep learning (DL) content from the past 24 hours.
Google Enhances Machine Learning Services
Google launches a dedicated team to drive Google Cloud Machine Learning, and announces a slew of enhancements to its services:
— Google Cloud Jobs API provides businesses with capabilities to find, match and recommend jobs to candidates. Currently available in a limited alpha.
— In 2017, GPU-accelerated instances will be available for the Google Compute Engine and Google Cloud Machine Learning. Details here.
— Cloud Translation API will be available in two editions, Standard and Premium.
— Cloud Natural Language API graduates to general availability.
Microsoft Partners for AI
— In Forbes, investor Jason Black offers a hype-free explanation of machine learning.
— Sam DeBrule provides a non-technical guide to machine learning and artificial intelligence.
— Camron Godbout delivers a simple description of deep learning.
Methods and Techniques
— In O’Reilly Radar, Reza Zadeh explains why deep learning is hard. In a word: optimization.
— Separately, S. Zayd Enam explains why machine learning is hard.
— Meanwhile, Matthew Honnibal proposes a four-step approach that makes natural language processing with deep learning easy.
— On the BigML blog, someone who blogs under the username “mariajesusbigml” introduces you to Topic Models.
— Martin Heller reviews Spark ML 2.01. He likes it.
— Separately, Martin reviews Microsoft Cognitive Toolkit (CNTK). He likes that, too. Looking back at Martin’s reviews of TensorFlow, Google Cloud Machine Learning, IBM Watson, and Amazon Machine Learning, I gather that Martin likes everything, except for HPE Haven, of course, but that’s a given. Oddly, Martin thinks Microsoft Azure Machine Learning is harder to use than the rest of this lot, which is nuts.
— Cray unveils deep learning toolkits for the Cray XC and Cray CS-Storm systems. Kits include Caffe, Microsoft Cognitive Toolkit (CNTK), MXNet, NVIDIA DIGITS (Deep Learning GPU Traning System), TensorFlow, and Torch.
— In Fast Company, Christina Farr describes a joint project of General Electric and the University of California – San Francisco to develop a library of machine learning algorithms for diagnostics in primary care, pathology, and radiology.
— Scientists at UCLA and the University of Illinois use machine learning to discover and design alpha-helical membrane-active peptides, which may lead to improved cancer therapies.
— In The Motley Fool, Timothy Green argues that NVIDIA is crushing AMD.
— DataRobot, a machine learning company, partners with data blending leader Alteryx.