BigDataFr recommends: Uniform random colored complexes […] We present here random distributions on (D+1)-edge-colored, bipartite graphs with a fixed number of vertices 2p. These graphs are dual to D-dimensional orientable colored complexes. We investigate the behavior of quantities related to those random graphs, such as their number of connected components or the number of vertices […]
Author: Big Data
[Analyticsvidhya] BigDataFr recommends: 10 Advanced Deep Learning Architectures Data Scientists Should Know!
BigDataFr recommends: 10 Advanced Deep Learning Architectures Data Scientists Should Know! Introduction […] It is becoming very hard to stay up to date with recent advancements happening in deep learning. Hardly a day goes by without a new innovation or a new application of deep learning coming by. However, most of these advancements are hidden […]
[Datasciencecentral] BigDataFr recommends: Do you want to hire a Data Scientist?
BigDataFr recommends: Do you want to hire a Data Scientist? […] As mentioned by Tom Davenport few years back,Data Scientist is still a hottest job of century. Data scientists are those elite people who solve business problems by analyzing tons of data and communicate the results in a very compelling way to senior leadership and […]
[Analyticbridge] BigDataFr recommends: Introducing User Behavioral Analysis in the Risk Process
BigDataFr recommends: Introducing User Behavioral Analysis in the Risk Process […] Many years ago when I was entering the intelligence community, I attended a class in Virginia where the instructor opened the session with a test that I will never forget and that I have applied to almost every analytic task in my career. At […]
[arXiv] BigDataFr recommends: Bayesian Nonlinear Support Vector Machines for Big Data
BigDataFr recommends: Bayesian Nonlinear Support Vector Machines for Big Data […] We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales easily to millions of data points. It provides additional […]
[Datasciencecentral] BigDataFr recommends: Better Banking with help of Analytics and Machine learning
BigDataFr recommends: Better Banking with help of Analytics and Machine learning […] In 2015, I was working at Diebold where we build ATM machine hardware and software and complete ecosystem around the ATM. When we talk about ATM machine, it is a collection of very complex small hardware which collectively performs tasks. And typically, when […]
[Datasciencecentral] BigDataFr recommends: Big data in Retail industry
BigDataFr recommends: Big data in Retail industry […] 1. Objective Through this blog you will know about the Big data applications and Big data uses in retail sector with examples along with use of RFID in retail. You will learn concepts related to Big data in retail industry like retail asset management, production tracking in […]
[arXiv] BigDataFr recommends: Big Data vs. complex physical models: a scalable inference algorithm
BigDataFr recommends: Big Data vs. complex physical models: a scalable inference algorithm […] The data torrent unleashed by current and upcoming instruments requires scalable analysis methods. Machine Learning approaches scale well. However, separating the instrument measurement from the physical effects of interest, dealing with variable errors, and deriving parameter uncertainties is usually an after-thought. Classic […]
[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 […]