lundi 11 mars 2019

Certificat de spécialisation Analyste de données massives à l'ISSAE Cnam Liban

Objectifs

Data Science à l'ISSAE Cnam Liban
Data scientist
Ce certificat offre la possibilité à des informaticiens, mathématiciens, statisticiens de suivre une formation professionnelle pluridisciplinaire pour acquérir les compétences propres à l'exercice du métier émergent de data scientist également appelé "analyste big data".
Alliant des compétences en mathématiques, statistique, informatique, visualisation de données ; il est capable de stocker, rechercher, capter, partager, interroger et donner du sens à d'énormes volumes d'informations: des données structurées et non structurées, produites en temps réel et provenant de sources diverses.
À l'ISSAE Cnam Liban un Certificat de spécialisation Analyste de données massives est proposé si vous êtes intéressé vous pouvez vous faire connaitre en remplissant ce formulaire : https://cnamliban.page.link/inscriptionDS
Ce certificat est disponible hors temps de travail, le soir ou le samedi.

mercredi 14 février 2018

Is Blogging Dead And Buried?



At some point or other you're bound to come across some naysayer who says blogging is dead. Blogging was very popular when it first began in around 2007. People could freely express themselves online without having to know a lot about building websites, HTML and the like.

Some continued to blog on a regular schedule. Most lost the motivation and gave up. Those are the people who say blogging is dead. Running a blog is a test of endurance and marketing skills, not just writing skills.

The Future Of Blogging

Today, it's never been easier to start a professional blog using software like WordPress. But just because you can start a blog doesn't mean you should. It's a significant commitment in terms of time and effort. That being said, the profits can be substantial for people who treat their blog into real business.

Article Source: http://EzineArticles.com/9883453

jeudi 26 janvier 2017

AI Software Learns to Make AI Software

Technology Review (01/18/17) Tom Simonite 

Several research organizations, including Google Brain and DeepMind, are working to create artificial intelligences (AI) that can in turn develop machine-learning software. In many cases, the results coming from machines programming other machines match or exceed work done by humans. If self-programming AI techniques become practical, they could increase the pace at which machine learning is adopted throughout the economy without requiring more machine-learning experts, who already are in short supply. One set of experiments from DeepMind suggests self-teaching methods could alleviate the problem of AI software needing to consume massive amounts of data on a specific task. Researchers challenged their software to create machine-learning systems for collections of multiple, related problems. The software produced designs that demonstrated an ability to generalize and adopt new tasks with less training. A team at the Massachusetts Institute of Technology (MIT) plans to open source the software behind their experiments, in which an AI designed deep-learning systems that matched systems made by humans on standard tests for object recognition. However, these techniques require extreme computer power and are not yet viable replacements for machine-learning experts. MIT Media Lab's Otkrist Gupta believes companies will be motivated to find ways to make automated machine learning practical.