BigDataFr recommends: How data science can turn the vision of connected vehicles into reality ‘As per NHTSA statistics, more than 32,000 people lost their lives in the United States in 2013 in road accidents. There is no better use for technology than saving lives. Connected vehicles represent a seismic movement that is ready for prime […]
Documentation
[O’R] BigDataFr recommends: What it means to “go pro” in data science #datascientist
BigDataFr recommends: What it means to “go pro” in data science ‘My experience of being a data scientist is not at all like what I’ve read in books and blogs. I’ve read about data scientists working for digital superstar companies. They sound like heroes writing automated (near sentient) algorithms constantly churning out insights. I’ve read […]
[HAL] BigDataFr recommends: NumaGiC – a Garbage Collector for Big Data on Big NUMA Machines #datascientist #machine learning
BigDataFr recommends: NumaGiC – a Garbage Collector for Big Data on Big NUMA Machines Introduction (excerpt) ‘Data-analytics programs require large amounts of computing power and memory. When run on modern multicore computers with a cache-coherent Non-Uniform Memory Access (ccNUMA) architecture, they suffer from a high overhead during garbage collection (GC) caused by a bad memory […]
[Blue Yonder] Algorithms Are the Engine of Digitalization
BigDataFr recommends: Algorithms Are the Engine of Digitalization ‘At the beginning of July, we held our fourth Big Data & Analytics Congress (we call it the Datalympics), where participants learned how to better master digital change. The focus was on how companies can drive their business-process optimization, for example using automated materials planning, Industry 4.0, […]
[FrenchWeb Day Startup – Video] BigDataFr recommande : eCommerce – mettre la data et le shopping analytics au service du client
BigDataFr recommande : eCommerce – mettre la data et le shopping analytics au service du client « A l’occasion du FrenchWeb Day Startup qui a eu lieu le 17 juin 2015, Frenchweb a interrogé David Bessis, le fondateur de Tinyclues, sur le thème: «E-commerce : Mettre la data et le shopping analytic au service du client». » […]
[kdnuggets]BigDataFr recommends: Can deep learning help find the perfect date? #machine learning
BigDataFr recommends: Can deep learning help find the perfect date? ‘When a Machine Learning PhD student at University of Montreal starts using Tinder, he soon realises that something is missing in the dating app – the ability to predict to which girls he is attracted. Harm de Vries applies Deep Learning to assist in the […]
[O’R] BigDataFr recommends: Handling Missing Data
BigDataFr recommends: Handling Missing Data ‘The difference between data found in many tutorials and data in the real world is that real-world data is rarely clean and homogeneous. In particular, many interesting datasets will have some amount of data missing. To make matters even more complicated, different data sources may indicate missing data in different […]
[La Tribune] BigDataFr recommande : IBM accélère dans le big data et la santé
BigdataFr recommande : IBM accélère dans le big data et la santé « Watson, le système d’intelligence artificielle d’IBM, qui est capable d’analyser en quelques secondes des quantités massives de données, va bientôt ajouter une nouvelle corde à son arc. En effet, le groupe américain a racheté Merge Healthcare, jeudi 6 août, et sa plateforme de […]
[reputationvip] BigDataFr recommande : Big data – l’avenir du recrutement ?
BigDataFr recommande : Big data – l’avenir du recrutement ? « Cette histoire nous vient des États-Unis. Jade Dominguez, 26 ans, habitant de South Pasadena en Californie, n’est jamais allé à l’Université et a appris la programmation en autodidacte. Il a pourtant reçu une offre pour devenir programmeur par une start-up de San Francisco et a […]
[Mckinsey] BigDataFr recommends: An executive’s guide to machine learning #machine learning
BigDataFr recommends: An executive’s guide to machine learning ‘Machine learning is based on algorithms that can learn from data without relying on rules-based programming. It came into its own as a scientific discipline in the late 1990s as steady advances in digitization and cheap computing power enabled data scientists to stop building finished models and […]