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Skapánê
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Skapánê est une entreprise de services spécialisée dans les traitements Big Data et Machine Learning en temps réel.
Skapánê est une entreprise de services spécialisée dans les traitements Big Data et Machine Learning en temps réel.

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C'est fait ! 😀 Le GONG est sonné ! Merci à toutes les personnes qui nous soutiennent et nous suivent !
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Pour connaître davantage +Skapánê --> notre vidéo 360° !

https://www.youtube.com/watch?v=3qoIEGEZ0-Y
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Les 10 erreurs sur le Big Data ! A lire ! ;)
10 of the Most Common Big Data Mistakes (https://goo.gl/YPLssd)

As the world becomes more immersed in technology and computing, we are generating more and more data on a daily basis. And in the world of business and enterprise companies, this data is proving to be incredibly valuable.

And with the recent craze of applying big data and analytics to just about every kind of business imaginable, it's a booming business as well.

But before you rush headlong into it and scramble to hire data scientists to create useful data, +Superb Internet has a few pointers. Specifically 10 mistakes you would be wise to avoid.

Here's a good one:

"Too Much Data, Not Enough Science: Business is obviously fundamentally concerned with capital, but you can't profit from data science without using basic scientific principles."

"When you approach data science, in other words, you need to root out any potential bias. You come up with an objective and a hypothesis - basically question and possible answer. What do you want to know, and what do you suspect is true?"

"Furthermore, you need to make sure that your data is of high enough quality that you aren't being misled."

Click here to read the rest of article and read the other nine mistakes you should avoid when it comes to big data: https://goo.gl/YPLssd

#business #data #bigdata
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L'équipe de +Skapánê au complet !
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Découvrez le nouveau site de Skapánê ! :)
www.skapane.com
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With the recent machine learning boom, more and more algorithms have become available that perform exceptionally well on a number of tasks. But knowing beforehand which algorithm will perform best on your specific problem is often not possible. If you had infinite time at your disposal, you could just go through all of them and try them out. The following guide shows you a better way to do this, step by step, by relying on known techniques from model selection and hyper-parameter tuning.
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