BigDataFr recommends: Innovating Faster on Personalization Algorithms at Netflix Using Interleaving […] The Netflix experience is powered by a family of ranking algorithms, each optimized for a different purpose. For instance, the Top Picks row on the homepage makes recommendations based on a personalized ranking of videos, and the Trending Now row also incorporates recent […]
Technologies
[MIT Technology Review] BigDataFr recommends: Google Has Released an AI Tool That Makes Sense of Your Genome
BigDataFr recommends: Google Has Released an AI Tool That Makes Sense of Your Genome […] Almost 15 years after scientists first sequenced the human genome, making sense of the enormous amount of data that encodes human life remains a formidable challenge. But it is also precisely the sort of problem that machine learning excels at. […]
[Dataquest .io] BigDataFr recommends: Regular Expressions for Data Scientists
BigDataFr recommends: Regular Expressions for Data Scientists […] As data scientists, diving headlong into huge heaps of data is part of the mission. Sometimes, this includes massive corpuses of text. For instance, suppose we were asked to figure out who’s been emailing whom in the scandal of the Panama Papers — we’d be sifting through […]
[HAL] BigDataFr recommends: Predicting At-Risk Patient Profiles from Big Prescription Data
BigDataFr recommends: Predicting At-Risk Patient Profiles from Big Prescription Data Abstract […] We show how the analysis of very large amounts of drug prescription data make it possible to detect, on the day of hospital admission, patients at risk of developing complications during their hospital stay. We explore, for the first time, to which extent […]
[Datasciencecentral] BigDataFr recommends: Some Deep Learning with Python, TensorFlow and Keras
BigDataFr recommends: Some Deep Learning with Python, TensorFlow and Keras […] The following problems are taken from a few assignments from the coursera courses Introduction to Deep Learning (by Higher School of Economics) and Neural Networks and Deep Learning (by Prof Andrew Ng, deeplearning.ai). The problem descriptions are taken straightaway from the assignments. 1. Linear […]
[arXiv] BigDataFr recommends: A Big Data Analysis Framework Using Apache Spark and Deep Learning
BigDataFr recommends: A Big Data Analysis Framework Using Apache Spark and Deep Learning […] Subjects: Databases (cs.DB); Learning (cs.LG); Machine Learning (stat.ML) With the spreading prevalence of Big Data, many advances have recently been made in this field. Frameworks such as Apache Hadoop and Apache Spark have gained a lot of traction over the past […]
[Dataconomy] BigDataFr recommends: How big data technology is transforming fraud investigations
BigDataFr recommends: How big data technology is transforming fraud investigations […]The inside story of the Paradise Papers leak With more than 1,4 terabytes of data, the Paradise Papers is the illustration of the new possibilities for the fraud investigation world. Conducting investigations is a challenge in the age of big data. Massive volumes, unstructured and […]
[MIT Technology Review] BigDataFr recommends: IBM Raises the Bar with a 50-Qubit Quantum Computer
BigDataFr recommends: IBM Raises the Bar with a 50-Qubit Quantum Computer […] IBM established a landmark in computing Friday, announcing a quantum computer that handles 50 quantum bits, or qubits. The company is also making a 20-qubit system available through its cloud computing platform. IBM, Google, Intel, and a San Francisco startup called Rigetti are […]
[Datasciencecentral] BigDataFr recommends: High Precision Computing: Benchmark, Examples, and Tutorial
BigDataFr recommends: High Precision Computing: Benchmark, Examples, and Tutorial […] In some applications, using the standard precision in your programming language of choice, may not be enough, and can lead to disastrous errors. In some cases, you work with a library that is supposed to provide very high precision, when in fact the library in […]
[arXiv] BigDataFr recommends: Learning to Predict with Big Data
BigDataFr recommends: Learning to Predict with Big Data […] Subjects: Big spatio-temporal datasets, available through both open and administrative data sources, offer significant potential for social science research. The magnitude of the data allows for increased resolution and analysis at individual level. One of the issues researchers face with such data is the stationarity assumption. […]