Original Post: Portrait mode on the Pixel 2 and Pixel 2 XL smartphones
[unable to retrieve full-text content]In this eBook, you will learn how NVIDIA DGX Systems offer the fastest path to AI and deep learning, how to spend more time focused on experimentation and less time wrestling with IT, and using DGX Systems include access to NVIDIA-optimized deep learning frameworks.
Original Post: Download NVIDIA DGX Systems eBook
[unable to retrieve full-text content]Seeking a BI-Analyst, to bring domain knowledge and experience to a diverse team and contribute to the understanding and usage of data throughout the company and to enable data driven decision making.
Original Post: Jimdo: Business Intelligence Analyst
[unable to retrieve full-text content]Random Forest, one of the most popular and powerful ensemble method used today in Machine Learning. This post is an introduction to such algorithm and provides a brief overview of its inner workings.
Original Post: Random Forests(r), Explained
[unable to retrieve full-text content]We examine the implications of trends in hiring market, including the growth of quantitative Initiatives, blurring of the lines between Predictive Analytics and Data Science Professionals, and more .
Original Post: 4 Major Trends Influencing the 2017 Predictive Analytics Hiring Market
[unable to retrieve full-text content]In this article, I will explore natural stupidity in more detail and show how our current technology (driven by narrow artificial intelligence) is making us collectively dumber.
Original Post: Natural Stupidity is more Dangerous than Artificial Intelligence
[unable to retrieve full-text content]At the Deep Learning Summit in Montreal last week, we saw Yoshua Bengio, Yann LeCun and Geoffrey Hinton come together to share their most cutting edge research progressions as well as discussing the landscape of AI and the deep learning ecosystem in Canada.
Original Post: RE•WORK Deep Learning Summit Montreal Panel of Pioneers Interview: Yoshua Bengio, Yann LeCun, Geoffrey Hinton
What a wonderful week at the Astrostat [Indian] summer school in Autrans! The setting was superb, on the high Vercors plateau overlooking both Grenoble [north] and Valence [west], with the colours of the Fall at their brightest on the foliage of the forests rising on both sides of the valley and a perfect green on the fields at the centre, with sun all along, sharp mornings and warm afternoons worthy of a late Indian summer, too many running trails [turning into X country ski trails in the Winter] to contemplate for a single week [even with three hours of running over two days], many climbing sites on the numerous chalk cliffs all around [but a single afternoon for that, more later in another post!]. And of course a group of participants eager to learn about Bayesian methodology and computational algorithms,…
Original Post: Astrostatistics school
A new rOpenSci package provides access to data to which users may already have directly contributed, and for which contribution is fun, keeps you fit, and helps made the world a better place. The data come from using public bicycle hire schemes, and the package is called bikedata. Public bicycle hire systems operate in many cities throughout the world, and most systems collect (generally anonymous) data, minimally consisting of the times and locations at which every single bicycle trip starts and ends. The bikedata package provides access to data from all cities which openly publish these data, currently including London, U.K., and in the U.S.A., New York, Los Angeles, Philadelphia, Chicago, Boston, and Washington DC. The package will expand as more cities openly publish their data (with the newly enormously expanded San Francisco system next on the list). The short…
Original Post: Data from Public Bicycle Hire Systems
One of our senior data scientists, Olga Mierzwa-Sulima spoke at the userR! conference in Brussels to a packed house. The seats were full and there were audience members spilling out the doors. Source: https://twitter.com/matlabulous/status/882530484374392834 Olga’s talk was entitled ‘How we built a Shiny App for 700 users?’ She went over the main challenges associated with scaling a Shiny application, and the methods we used to resolve them. The talk was partly in the form of a case study based on Appsilon’s experience. In this talk, Olga shared her experience from a real-life case study of building an app used daily by 700 users where our data science team tackled all these problems. This, to our knowledge, was one of the biggest production deployments of a Shiny App. Shiny has proved itself a great tool for communicating data science teams’ results.…
Original Post: How we built a Shiny App for 700 users?