BigDataFr recommande: Big Data : et si une des réponses était le mainframe ? […] Petite question : qu’est-ce qui a plus de 60 ans, mais est toujours suffisamment alerte pour battre les meilleurs clusters milieu de gamme au petit jeu du Big Data ? Si vous avez répondu le mainframe, vous avez vu juste. […]
Innovation
[Datasciencecentral] BigDataFr recommends: Data Team for Data Driven Organization
BigDataFr recommends: Data Team for Data Driven Organization A data-driven organization will use the data as critical evidence to help inform and influence strategy. To be data-driven means cultivating a mindset throughout the business to continually use data and analytics to make fact-based business decisions. Becoming a data-driven organization is no longer a choice, but […]
[HAL] BigDataFr recommends: Týr: Efficient Transactional Storage for Data-Intensive Applications
BigDataFr recommends: Týr: Efficient Transactional Storage for Data-Intensive Applications […] As the computational power used by large-scale applications increases, the amount of data they need to manipulate tends to increase as well. A wide range of such applications requires robust and flexible storage support for atomic, durable and concurrent transactions. Historically, databases have provided the […]
[economiematin] BigDataFr recommande : AlphaGo, une nouvelle victoire de Google pour l’intelligence artificielle
BigDataFr recommande: AlphaGo, une nouvelle victoire de Google pour l’intelligence artificielle […] Tout de go? Tout d’abord, revenons sur les conditions de cette victoire. Le jeu de go est d’origine chinoise et compte plusieurs milliers d’années d’existence derrière lui. Outre son âge vénérable, il est aussi connu pour sa complexité. On estime ainsi à 10 […]
[Deepmind] BigDataFr recommends: Mastering the game of go with deep neural networks and tree search
BigDataFr recommends: Mastering the game of go with deep neural networks and tree search This paper published in Nature on 28th January 2016, describes a new approach to computer Go that combines Monte-Carlo tree search with deep neural networks that have been trained by supervised learning, from human expert games, and by reinforcement learning from […]
[arXiv] BigDataFr recommends: Security and Privacy Issues of Big Data
BigDataFr recommande: Security and Privacy Issues of Big Data […]This chapter revises the most important aspects in how computing infrastructures should be configured and intelligently managed to fulfill the most notably security aspects required by Big Data applications. One of them is privacy. It is a pertinent aspect to be addressed because users share more […]
[France Culture] BigDataFr recommande : Le « Deep Learning », ou l’apprentissage profond des machines
BigDataFr recommends: Le « Deep Learning », ou l’apprentissage profond des machines […] Le « deep learning » ou « apprentissage profond », va-t-il entraîner une quatrième révolution industrielle ? Rencontre avec « le Français qui apprend l’intelligence artificielle à Facebook », Yann Le Cun. Intervenants : Yann Le Cun : directeur du laboratoire d’Intelligence Artificielle de […]
[Dataconomy] BigDataFr recommends: Free Resources to get your Data Science Career started
BigDataFr recommends: Free Resources to get your Data Science Career started […] Data Scientist are in high demand, ranked the #1 profession in America on Glassdoor. And according to Forbes, an additional 1,700 job openings paying an average salary of $116,000 US dollars are available, contributing to an exponential expansion of the field. So are […]
[Rapport] BigDataFr recommande : Rapport au Premier ministre sur la gouvernance de la donnée 2015
BigDataFr recommande: Rapport au Premier ministre sur la gouvernance de la donnée 2015 Introduction Prédire et empêcher les vols de voitures ; optimiser les temps d’attente aux urgences ; mieux cibler les contrôles douaniers ; détecter les immeubles passoires énergétiques ; repérer les entreprises qui vont prochainement recruter et les signaler aux demandeurs d’emploi concernés […]
[Dataconomy] BigDataFr recommends: Understanding Dimensionality Reduction and its Applications
BigDataFr recommends: Understanding Dimensionality Reduction and its Applications Dimensionality reduction as means of feature extraction Feature extraction is a very broad and essential area of data science. It’s goal is to take out salient and informative features from input data, so that they can be used further in predictive algorithms. Modern data scientists observe large […]