BigDataFr recommends: Big Data analytics. Three use cases with R, Python and Spark Subjects: Applications (stat.AP); Learning (cs.LG) […] Management and analysis of big data are systematically associated with a data distributed architecture in the Hadoop and now Spark frameworks. This article offers an introduction for statisticians to these technologies by comparing the performance obtained […]
Innovation
[Datasciencecentral] BigDataFr recommends: How Data Is Changing the Future of Smart Health Technology
BigDataFr recommends: How Data Is Changing the Future of Smart Health Technology […] “Many products and applications exist today that can help us take steps toward healthier living. » – Ben Bajarin Big data is everywhere. Yes it’s true that it has always been there but, we are just recently finding ways to best utilize it. […]
[arXiv] BigDataFr recommends: The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data
BigDataFr recommends: The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data Subjects: Methodology (stat.ME); Computation (stat.CO) […] This paper introduces a class of Monte Carlo algorithms which are based upon simulating a Markov process whose quasi-stationary distribution coincides with the distribution of interest. This differs fundamentally from, say, current Markov chain Monte Carlo in […]
[arXiv] BigDataFr recommends: Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework
BigDataFr recommends: Human-Algorithm Interaction Biases in the Big Data Cycle: A Markov Chain Iterated Learning Framework Comments: This research was supported by National Science Foundation grant NSF-1549981 Subjects: Learning (cs.LG); Human-Computer Interaction (cs.HC) […] Early supervised machine learning algorithms have relied on reliable expert labels to build predictive models. However, the gates of data generation […]
[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 […]
[Datasciencecentral – Top] BigDataFr recommends: 40 Techniques Used by Data Scientists
BigDataFr recommends: 40 Techniques Used by Data Scientists […] These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools. When you click on any of the 40 links below, you will find a selection […]
[Datasciencecentral] BigDataFr recommends: The challenges of word embeddings #deeplearning
BigDataFr recommends: The challenges of word embeddings […] In recent times deep learning techniques have become more and more prevalent in NLP tasks; just take a look at the list of accepted papers at this year’s NAACL conference, and you can’t miss it. We’ve now completely moved away from traditional NLP approaches to focus on […]
[Datasciencecentral] BigDataFr recommends: Hitchhiker’s Guide to Data Science, Machine Learning, R, Python
BigDataFr recommends: Hitchhiker’s Guide to Data Science, Machine Learning, R, Python […] Thousands of articles and tutorials have been written about data science and machine learning. Hundreds of books, courses and conferences are available. You could spend months just figuring out what to do to get started, even to understand what data science is about. […]
[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 […]
[Data Science Central] BigDataFr recommends: Data Scientists Automated and Unemployed by 2025!
BigDataFr recommends: Data Scientists Automated and Unemployed by 2025! […] In a recent poll the question was raised “Will Data Scientists be replaced by software, and if so, when?” The consensus answer: Data Scientists automated and unemployed by 2025. Are we really just grist for the AI mill? Will robots replace us? As part of […]