[unable to retrieve full-text content]Strata Data Conference is where thousands of innovators, leaders, and practitioners gather to develop new skills, share best practices, and discover how tools and technologies are evolving. Best rate ends Dec 8 – use code PCKDNG to save.
Original Post: Strata Data Conference, San Jose, Mar 5-8, 2018 – KDnuggets Offer
[unable to retrieve full-text content]Strata is a conference I very much enjoyed attending. This year, I observed a few common themes that ran across much of the conference content: Data Science Collaboration, Data Ethics, and Platform Optimization.
Original Post: Strata Data Conference, NYC – Key Trends and Highlights
[unable to retrieve full-text content]Data is driving business transformation. Come to Strata Data Conference and learn how to turn algorithms into business advantage, build modern data strategies, and spend quality time with experts. Use code KDNU to save.
Original Post: Strata Data Conference – 3 reasons to attend, Sep 25-28, NYC
[unable to retrieve full-text content]Strata Data Conference, the annual reunion of data brain trust, is Sept 25-28 in New York. Early price ends Aug 11 – save more with code KDNU.
Original Post: Strata Data Conference, the reunion of data brain trust – KDnuggets Offer
[unable to retrieve full-text content]Cutting-edge science and new business fundamentals intersect and merge at Strata Data Conference. Win KDnuggets Pass – submit your entry by Aug 17, 2017.
Original Post: Win KDnuggets Free Pass to Strata Data Conference NYC, Sep 25-28, 2017
[unable to retrieve full-text content]Data Science expert Mikio Braun on the anatomy of an architecture to bring data science into production. Learn more at his talk at Strata NYC – Use code KDNU for additional 20% off (best price ends Aug 11).
Original Post: What is hardcore data science – in practice?
By Wahid Bhimji, NERSC This is part 3, a continuation of the post on Big Data Ecosystem for Science and Big Data Ecosystem for Science: Genomics. Primary Collaborator: Shane Cannon (LBNL) Introduction Climate change is one of the most pressing challenges for human society in the 21st century. Studying how the climate system has evolved over the latter half of the 20th century is largely enabled by a combination of conventional weather stations, ocean sensors, and global satellites. To better understand future climate regimes, we must turn to high-fidelity simulations. CAM5 (Community Atmospheric Model) is a state-of-the-art climate model that, when run at 25-km resolution for a simulated period of three decades, produces more than 100 TB of complex, multivariate data. The 2007 Nobel Peace Prize was awarded to the Intergovernmental Panel on Climate Change (IPCC), which analyzed terabytes of…
Original Post: The big data ecosystem for science: Climate Science and Climate Change
By Wahid Bhimji, NERSC This is part 2, a continuation of the post on Big Data Ecosystem for Science. Primary collaborator: Debbie Bard (LBNL)Genomics and DNA sequencers Introduction The field of genomics has undergone a revolution over the past decade as the cost of sequencing has rapidly declined and the practice of sequencing has been commoditized. These advances are enabling discoveries in all areas of biology and have broad applications in cancer research, personalized medicine, bio-energy, and genetic engineering, just to name a few. The field of genomics is too broad to quickly capture the range of inquiry and discovery that is being enabled, but I will briefly describe some of the relevant technologies and analysis patterns below. Data ingestion One of the leading sources of data in the genomic space is a sequencer that can extract the DNA sequence…
Original Post: The big data ecosystem for science: Genomics
[unable to retrieve full-text content]Big Data management is essential for experimental science and technologies used in various science communities often predate those in Big Data industry and in many cases continue to develop independently. This post highlights some of these technologies, focusing on those used by several projects supported by the National Energy Research Scientific Computing Centre (NERSC).
Original Post: The big data ecosystem for science: Physics, LHC, and Cosmology
It’s easy to optimize simple neural networks, let’s say single layer perceptron. But, as network becomes deeper, the optmization problem becomes crucial. This article discusses about such optimization problems with deep neural networks. By Reza Zadeh, Founder and CEO of Matroid. At the heart of deep learning lies a hard optimization problem. So hard that for several decades after the introduction…
Original Post: The hard thing about deep learning