BigDataFr recommande: Comment les jeux se jouent-ils de nous ? Pokémon, économie des données et analyse comportementale Pokémon Go bat donc tous les records. Le plus grand nombre de téléchargements des magasins d’applications, le plus grand nombre d’utilisateurs en une semaine, etc. Et les médias se régalent de ces images de masses de joueurs collés […]
Actualités
[Quora – ITV] BigDataFr recommends: Session With Yann LeCun – AI, Deep Learning – Machine Learning
BigDataFr recommends: Session With Yann LeCun – AI, Deep Learning – Machine Learning Interview with Yann Le Cun, Director of AI Research at Facebook and Professor at NYU What are some recent and potentially upcoming breakthroughs in deep learning? There are many interesting recent development in deep learning, probably too many for me to describe […]
[Informationweek] BigDataFr recommends: 9 Tips For Hiring Data Science Talent
BigDataFr recommends: 9 Tips For Hiring Data Science Talent Data science and data analytics skills are in high demand, and the race is on to attract and hire top-notch professionals who possess these skill sets. That’s no surprise. Establishing a data science practice inside an organization can provide a competitive edge against rivals in an […]
[Dataconomy] BigDataFr recommends: BigDataFr recommends: Getting into Data Science: A Guide for Students and Parents
BigDataFr recommends: Getting into Data Science: A Guide for Students and Parents […] As can likely be expected, big data analytics is in the midst of an evolution. It’s a typical sight for nearly any new technology as experts and organizations get more used to all of its capabilities. Big data is certainly no exception […]
[Craig Kerstiens] BigDataFr recommends: Five Mistakes Beginners Make When Working With Databases
BigDataFr recommends: Five Mistakes Beginners Make When Working With Databases […] When you start out as a developer there’s an overwhelming amount of things to grasp. First there’s the language itself, then all the quirks of the specific framework you’re using,and after that (or maybe before) we’ll throw front-end development into the mix, and somewhere […]
[Fastcompany] BigDataFr recommends: Data.World Aims To Be The Social Network For Data Nerds
BigDataFr recommends: Data.World Aims To Be The Social Network For Data Nerds […] If the early to mid aughts were about developing all-encompassing social networks for everyone, now it’s about launching whole platforms targeted at a specific industry or subject—think of them like the chat rooms and message boards of web 1.0 but on steroids. […]
[Variance Explained] BigDataFr recommends: Does sentiment analysis work? A tidy analysis of Yelp reviews
BigDataFr recommends: Does sentiment analysis work? A tidy analysis of Yelp reviews […] This year Julia Silge and I released the tidytext package for text mining using tidy tools such as dplyr, tidyr, ggplot2 and broom. One of the canonical examples of tidy text mining this package makes possible is sentiment analysis. Sentiment analysis is […]
[Xeneta] BigDataFr recommends: Boosting Sales With Machine Learning
BigDataFr recommends: Boosting Sales With Machine Learning […] In this blog post I’ll explain how we’re making our sales process at Xeneta more effective by training a machine learning algorithm to predict the quality of our leads based upon their company descriptions. Head over to GitHub if you want to check out the script immediately, […]
[Wired] BigDataFr recommends: Big Privacy Ruling Says Feds Can’t Grab Data Abroad With a Warrant
BigDataFr recommends: Big Privacy Ruling Says Feds Can’t Grab Data Abroad With a Warrant […] An appeals court just sent the American Justice Department a clear message about its ability to reach beyond US borders to collect data with a search warrant: Keep your hands to yourself. In Thursday’s landmark ruling, a panel of Second […]
[arXiv] BigDataFr recommends: Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization
BigDataFr recommends: Asynchronous Parallel Algorithms for Nonconvex Big-Data Optimization: Model and Convergence Subjects: Optimization and Control (math.OC); Distributed, Parallel, and Cluster Computing (cs.DC) […] We propose a novel asynchronous parallel algorithmic framework for the minimization of the sum of a smooth nonconvex function and a convex nonsmooth regularizer, subject to both convex and nonconvex constraints. […]