BigDataFr recommends: A methodology for solving problems with DataScience for Internet of Things – Part 1 and 2 […] This two part blog is based on my forthcoming book: Data Science for Internet of Things. It is also the basis for the course I teach Data Science for Internet of Things Course. I will be […]
Documentation
[arXiv] BigDataFr recommends: Representation of functions on big data associated with directed graphs
BigDataFr recommends: Representation of functions on big data associated with directed graphs Subjects: Classical Analysis and ODEs (math.CA) […] This paper is an extension of the previous work of Chui, Filbir, and Mhaskar (Appl. Comput. Harm. Anal. 38 (3) 2015:489-509), not only from numeric data to include non-numeric data as in that paper, but also […]
[Analyticsvidhya] BigDataFr recommends: 12 Winning Tips to Clinch Your First Win in Data Science Competitions
BigDataFr recommends: 12 Winning Tips to Clinch Your First Win in Data Science Competitions […] So, what are you doing this weekend ? We have an amazing opportunity you wouldn’t want to miss (if you are crazy about machine learning). Brace yourself up, the action is about to get begin. The Smart Recruits is round […]
[Datasciencecentral] BigDataFr recommends: Ableism in the Numbers – Social Metrification #datascientist
BigDataFr recommends: Ableism in the Numbers – Social Metrification […] Ableism (able + ism) is apparent in many interactions between people. While driving on a road having a posted limit of 60 KPH, I was traveling slower since I expected a red light to soon appear ahead of me. The driver behind me – at […]
[arXiv] BigDataFr recommends: Measuring Economic Activities of China with Mobile Big Data
BigDataFr recommends: Measuring Economic Activities of China with Mobile Big Data Subjects: Social and Information Networks (cs.SI); Computers and Society (cs.CY) […] Emerging trends in smartphones, online maps, social media, and the resulting geo-located data, provide opportunities to collect traces of people’s socio-economical activities in a much more granular and direct fashion, triggering a revolution […]
[Institut Mines Telecom] BigDataFr recommande : Quelles promesses et quels défis pour l’intelligence artificielle ?
BigDataFr recommande: Quelles promesses et quels défis pour l’intelligence artificielle ? […] Nouvel opus des cahiers de veille de la Fondation Télécom avec une grande thématique transversale : les intelligences artificielles. Ce cahier (8e de la série) fait un état de l’art de la recherche en IA dans le monde et présente les nombreuses recherches […]
[Dataconomy] BigDataFr recommends: 10 Online Big Data Courses and where to find them – 2016
BigDataFr recommends:10 Online Big Data Courses and where to find them – 2016 […] Who doesn’t want to learn about data scientist these days? The field is still hot, and the ample job listings for data scientists might make folks working in other fields instantly jealous. For young students, there are full degree programs and […]
[Datasciencecentral] BigDataFr recommends: Applications of Deep Learning
BigDataFr recommends: Applications of Deep Learning […] This post highlights a number of important applications found for deep learning so far. It is well known that 80% of data is unstructured. Unstructured data is the messy stuff every quantitative analyst tries to traditionally stay away from. It can include images of accidents, text notes of […]
[HAL] BigDataFr recommends: Benchmarking Hadoop Performance in the Cloud – An in Depth Study
BigDataFr recommends : Benchmarking Hadoop Performance in the Cloud – An in Depth Study of Resource Management and Energy Consumption Keywords: Cloud Computing, Virtualization, Green Consumption, Docker Container, Hadoop, Resources Consumption. Abstract […] Virtual technologies have proven their capabilities to ensure good performance in the context of high performance computing (HPC). During the last decade, […]
[arXiv] BigDataFr recommends: Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis
BigDataFr recommends: Big IoT and social networking data for smart cities: Algorithmic improvements on Big Data Analysis in the context of RADICAL city applications Subjects: Computers and Society (cs.CY); Learning (cs.LG); Social and Information Networks (cs.SI) […] In this paper we present a SOA (Service Oriented Architecture)-based platform, enabling the retrieval and analysis of big […]