BigDataFr recommends: Ableism in the Numbers – Social Metrification […] Ableism (able + ism) is apparent in many interactions between people. While driving on a road having a posted limit of 60 KPH, I was traveling slower since I expected a red light to soon appear ahead of me. The driver behind me – at […]
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[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 […]
[arXiv] BigDataFr recommends: Limited Random Walk Algorithm for Big Graph Data Clustering
BigDataFr recommends: Limited Random Walk Algorithm for Big Graph Data Clustering Subjects: Social and Information Networks (cs.SI); Physics and Society (physics.soc-ph) […]Graph clustering is an important technique to understand the relationships between the vertices in a big graph. In this paper, we propose a novel random-walk-based graph clustering method. The proposed method restricts the reach […]
[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 […]
[arXiv – MSc Thesis] BigDataFr recommends: LLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios
BigDataFr recommends: LLFR: A Lanczos-Based Latent Factor Recommender for Big Data Scenarios Subjects: Machine Learning (stat.ML); Information Retrieval (cs.IR); Social and Information Networks (cs.SI) […]The purpose if this master’s thesis is to study and develop a new algorithmic framework for Collaborative Filtering to produce recommendations in the top-N recommendation problem. Thus, we propose Lanczos Latent […]
[arXiv] BigDataFr recommends: Big Data Analytics Using Cloud and Crowd
BigDataFr recommends: Big Data Analytics Using Cloud and Crowd […]The increasing application of social and human-enabled systems in people’s daily life from one side and from the other side the fast growth of mobile and smart phones technologies have resulted in generating tremendous amount of data, also referred to as big data, and a need […]
[arXiv] BigDataFr recommends: From Big Data To Important Information
BigDataFr recommends: From Big Data To Important Information […]Advances in science are being sought in newly available opportunities to collect massive quantities of data about complex systems. While key advances are being made in detailed mapping of systems, how to relate this data to solving many of the challenges facing humanity is unclear. The questions […]
[Data Science Central] BigDataFr recommends: Citizen Data Scientist – Care, Feeding and Control
BigDataFr recommends: Citizen Data Scientist – Care, Feeding and Control […]Thanks to Gartner the term ‘Citizen Data Scientist’ has become ingrained in our professional literature, in the strategies of analytic platform developers, and even in the strategies of companies committed to advanced analytics. Gartner also recently forecast that the ranks of the Citizen Data Scientist […]
[arXiv] BigDataFr recommends: Crisis Analytics: Big Data Driven Crisis Response
BigDataFr recommends: Crisis Analytics: Big Data Driven Crisis Response […]Disasters have long been a scourge for humanity. With the advances in technology (in terms of computing, communications, and the ability to process and analyze big data), our ability to respond to disasters is at an inflection point. There is great optimism that big data tools […]
[Data Science Central] BigDataFr recommends: The Life of a Data Scientist ( Data Land Scape)
BigDataFr recommends: The Life of a Data Scientist ( Data Land Scape) […] Data scientists are big data wranglers. They take an enormous mass of messy data points (unstructured and structured) and use their formidable skills in math, statistics and programming to clean, massage and organize them. Then they apply all their analytic powers – […]