BigDataFr recommande : « Data scientist est un métier qui demande énormément de neurones » […] Selon le baromètre LinkedIn pour « Le Monde Campus », les jeunes diplômés en data science sont les profils les plus recherchés par les recruteurs français présents sur le réseau social. Serge Abiteboul, chercheur à l’Institut national de recherche […]
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[HAL] BigDataFr recommande: Quels réseaux pour les Big Data?
BigDataFr recommande: Quels réseaux pour les Big Data? Résumé […] Un déluge de données est à prévoir dans les années à venir. Les utilisateurs de réseaux sociaux et l’Internet des objets, pour ne citer que ces deux exemples, génèrent déjà de grands volumes de données variées qui devront être transmises, enregistrées et traitées à grande […]
[Analyticsvidhya] BigDataFr recommends: 42 Questions on SQL for all aspiring Data Scientists
BigDataFr recommends: 42 Questions on SQL for all aspiring Data Scientists Introduction […] SQL is a universal tool in data science. Irrespective of which language you use as your main tool – you need to know SQL. There are no 2 ways about it. For most of the organisations, SQL is the way to store […]
[Analyticsvidhya] BigDataFr recommends: 5 AI applications in Banking to look out for in next 5 years
BigDataFr recommends: 5 AI applications in Banking to look out for in next 5 years “Machine intelligence is the last invention that humanity will ever need to make.”, Nick Bostrom Artificial intelligence is a reality today and it is impacting our lives faster than we can imagine. It is already present everywhere, from Siri in […]
[ArXiv] BigDataFr recommends: Big Data Analysis Using Shrinkage Strategies
BigDataFr recommends: Big Data Analysis Using Shrinkage Strategies […] In this paper, we apply shrinkage strategies to estimate regression coefficients efficiently for the high-dimensional multiple regression model, where the number of samples is smaller than the number of predictors. We assume in the sparse linear model some of the predictors have very weak influence on […]
[Big Data à Paris : Inscrivez-vous !] 30 mai 2017 – @MATLAB Expo France #DataAnalytics #MachineLearning #bigdata #machinelearning
Événement Big Data : 30 mai 2017 – MATLAB Expo France, une journée unique pour le Data Analytics et le Machine Learning. Conférence gratuite – inscription obligatoire : cliquer ici Vous vous intéressez aux thématiques du Big Data, Machine Learning, aux algorithmes d’analyse prédictive ou encore aux avancées en matière de Deep Learning ? Venez […]
[SAS] BigDataFr recommends: Which machine learning algorithm should I use?
BigDataFr recommends: Which machine learning algorithm should I use? […] This resource is designed primarily for beginning data scientists or analysts who are interested in identifying and applying machine learning algorithms to address the problems of their interest. A typical question asked by a beginner, when facing a wide variety of machine learning algorithms, is […]
[ArXiv] BigDataFr recommends: Best Practices for Applying Deep Learning to Novel Applications
BigDataFr recommends: Best Practices for Applying Deep Learning to Novel Applications […] This report is targeted to groups who are subject matter experts in their application but deep learning novices. It contains practical advice for those interested in testing the use of deep neural networks on applications that are novel for deep learning. We suggest […]
[Analyticsvidhya] BigDataFr recommends: 40 Questions on Probability for data science
BigDataFr recommends: 40 Questions on Probability for data science – [Solution: SkillPower – Probability, DataFest 2017] Introduction […] Probability forms the backbone of many important data science concepts from inferential statistics to Bayesian networks. It would not be wrong to say that the journey of mastering statistics begins with probability. This skilltest was conducted to […]
[Analyticsvidhya] BigDataFr recommends: Feature Engineering in IoT Age – How to deal with IoT data and create features for machine learning?
BigDataFr recommends: Feature Engineering in IoT Age – How to deal with IoT data and create features for machine learning? Introduction […] If you ask any experienced analytics or data science professional, what differentiates a good model from a bad model – chances are that you will hear a uniform answer. Whether you call it […]