[Datasciencecentral] BigDataFr recommends: Embracing Conflict to Fuel Digital Innovation

BigDataFr recommends: Embracing Conflict to Fuel Digital Innovation […] When talking to clients about their business goals, most business executives are pretty clear as to what they want to accomplish, such as reducing customer churn or reducing inventory costs or improving quality of care or improving product line profitability. But these “one dimensional” business initiatives […]

[arXiv] BigDataFr recommends: A K-means clustering algorithm for multivariate big data with correlated components

BigDataFr recommends: A K-means clustering algorithm for multivariate big data with correlated components […] Common clustering algorithms require multiple scans of all the data to achieve convergence, and this is prohibitive when large databases, with millions of data, must be processed. Some algorithms to extend the popular K-means method to the analysis of big data […]

[Datasciencecentral] BigDataFr recommends: What tomorrow’s business leaders need to know about Machine Learning?

BigDataFr recommends: What tomorrow’s business leaders need to know about Machine Learning? […] Sometimes I write a blog just to formulate and organize a point of view, and I think it’s time that I pull together the bounty of excellent information about Machine Learning. This is a topic with which business leaders must become comfortable, […]

[arXiv] BigDataFr recommends: From Big Data to Big Displays: High-Performance Visualization at Blue Brain

BigDataFr recommends: From Big Data to Big Displays: High-Performance Visualization at Blue Brain […] Blue Brain has pushed high-performance visualization (HPV) to complement its HPC strategy since its inception in 2007. In 2011, this strategy has been accelerated to develop innovative visualization solutions through increased funding and strategic partnerships with other research institutions. We present […]

[Google Research Blog] BigDataFr recommends: MultiModel: Multi-Task Machine Learning Across Domains

BigDataFr recommends: MultiModel: Multi-Task Machine Learning Across Domains […] Over the last decade, the application and performance of Deep Learning has progressed at an astonishing rate. However, the current state of the field is that the neural network architectures are highly specialized to specific domains of application. An important question remains unanswered: Will a convergence […]

[arXiv] BigDataFr recommends: Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models

BigDataFr recommends: Recipes for Translating Big Data Machine Reading to Executable Cellular Signaling Models […] With the tremendous increase in the amount of biological literature, developing automated methods for extracting big data from papers, building models and explaining big mechanisms becomes a necessity. We describe here our approach to translating machine reading outputs, obtained by […]

[arXiv] BigDataFr recommends: Big Data, Data Science, and Civil Rights

BigDataFr recommends: Big Data, Data Science, and Civil Rights […] Advances in data analytics bring with them civil rights implications. Data-driven and algorithmic decision making increasingly determine how businesses target advertisements to consumers, how police departments monitor individuals or groups, how banks decide who gets a loan and who does not, how employers hire, how […]

[Analyticbridge] BigDataFr recommends: The Most Important Lessons Learned from Data Science Projects

BigDataFr recommends: The Most Important Lessons Learned from Data Science Projects […] An overwhelming expansion of data archives posed a challenge to various industries, as these are now struggling to make use of such enormous amount of information. Almost 90% of all data ever recorded worldwide has been created in the last decade alone. This […]

[Datasciencecentral] BigDataFr recommends: Analytic Profiles: Key to Data Monetization

BigDataFr recommends: Analytic Profiles: Key to Data Monetization […]Many organizations are associating data monetization with selling their data. But selling data is not a trivial task, especially for organizations whose primary business relies on its data. Organizations new to selling data need to be concerned with privacy and Personally Identifiable Information (PII), data quality and […]