BigDataFr recommends: Using new models and big data to better understand financial risk […] The financial crisis of 2008, which saw the failure of major investment banks Bear Stearns and Lehman Brothers, and the subsequent government bailout of insurance giant American International Group (AIG), had a ripple effect around the globe. How did America’s housing […]
Documentation
[Code – Facebook] BigDataFr recommends: Introducing DeepText: Facebook’s text understanding engine #deeplearning
BigDataFr recommends: Introducing DeepText: Facebook’s text understanding engine? […] Text is a prevalent form of communication on Facebook. Understanding the various ways text is used on Facebook can help us improve people’s experiences with our products, whether we’re surfacing more of the content that people want to see or filtering out undesirable content like spam. […]
[Silicon] BigDataFr recommande : Big Data : IBM lance Data Science Experience, basé sur Spark
BigDataFr recommande : Big Data : IBM lance Data Science Experience, basé sur Spark […] IBM concrétise aujourd’hui son investissement dans l’outil Spark, fer de lance du Big Data analytique, via la mise en ligne de Data Science Experience sur sa plate-forme Cloud Bluemix. Au menu, 250 jeux de données, des outils Open Source et […]
[arXiv – Tip] BigDataFr recommends: D-SPACE4Cloud: A Design Tool for Big Data Applications
BigDataFr recommends: D-SPACE4Cloud: A Design Tool for Big Data Applications Subjects: Distributed, Parallel, and Cluster Computing […]The last years have seen a steep rise in data generation worldwide, with the development and widespread adoption of several software projects targeting the Big Data paradigm. Many companies currently engage in Big Data analytics as part of their […]
[HBR] BigDataFr recommends: Data Quality Should Be Everyone’s Job
BigDataFr recommande: Data Quality Should Be Everyone’s Job […]All of us depend on data created elsewhere to do our work. In the face of errors, most people’s natural reaction is to correct such errors in the data they need — after all, when you’re dealing with a mountain of day-in, day-out demands, that seems the […]
[arXiv] BigDataFr recommends: Sparse p-Adic Data Coding for Computationally Efficient and Effective Big Data Analytics
BigDataFr recommends: Sparse p-Adic Data Coding for Computationally Efficient and Effective Big Data Analytics […]We develop the theory and practical implementation of p-adic sparse coding of data. Rather than the standard, sparsifying criterion that uses the L0 pseudo-norm, we use the p-adic norm. We require that the hierarchy or tree be node-ranked, as is standard […]
[HAL] BigDataFr recommande : Résumer efficacement des flux de données massifs en fenêtre glissante
BigDataFr recommande : Résumer efficacement des flux de données massifs en fenêtre glissante Résumé […] Estimer la fréquence de n’importe quel item dans des flux de données massifs est un des problèmes majeurs de la dernière décennie. Si plusieurs solutions élégantes ont été proposées récemment, leur approximation est calculée depuis le commencement du flux. Dans […]
[Informationweek] BigDataFr recommends: 9 Free Online Courses To Pump Up Your Big Data, Analytics Skills
BigDataFr recommends: 9 Free Online Courses To Pump Up Your Big Data, Analytics Skills Analytics, big data, and data science are hot areas in the industry, and professionals who have these skills are in high demand. Some reports put annual salaries for data scientists at above the $200,000 mark. Career site Glass Door rated data […]
[Datasciencecentral] BigDataFr recommends: Deep Learning: Definition, Resources, Comparison with Machine Learning
BigDataFr recommends: Deep Learning: Definition, Resources, Comparison with Machine Learning […] Deep learning is sometimes referred to as the intersection between machine learning and artificial intelligence. It is about designing algorithms that can make robots intelligent, such a face recognition techniques used in drones to detect and target terrorists, or pattern recognition / computer vision […]
[Analyticsvidhya – Tutorial] BigDataFr recommends: Use H2O and data.table to build models on large data sets in R #machinelearning
BigDataFr recommends: 19 Data Science Tools for people who aren’t so good at Programming […] ‘Okay, I get it. data.table empowers us to do data exploration & manipulation. But, what about model building ? I work with 8GB RAM. Algorithms like random forest (ntrees = 1000) takes forever to run on my data set with 800,000 […]