Articles du blog / Blog Posts

In this blog post, we will build an Internet Of Thing (IoT) device based on the super cheap ESP8266 chip. The device is used to automate home shutters at a predefined time of the day or according to the house temperature in order to limit the temperature increase caused by sunlight.

In this blog post, we will see how we can use reverse image search based on (unsupervised) convolutional neural networks to make the analysis of satellite/aerial pictures both more efficient and simpler. After reading this post, you will be able to find similar objects in a large aerial/satellite images and from there develop your own GIS statistical applications (i.e. to count all white cars in your neighborhood, identify specific road markings or kind of trees, etc. ).

The requirement for a (very) large training set is generally the main critic that is formulated against deeplearning algorithms. In this blog, we show how deep convolutional neural networks (CNN) can be used in an unsupervised manner to identify similar images in a set.

In the space of Data Science development in enterprises, two outstanding scalable technologies are Spark and H2O. Spark is a generic distributed computing framework and H2O is a very performant scalable platform for AI. Their complementarity is best exploited with the use of Sparkling Water. The Spark-Notebook is an open source notebook (web-based environment for code edition, execution, and data visualization), focused on Scala and Spark. Working with Sparking Water in the Spark-Notebook scaffolds an ideal platform for big data /data science agile development.

In this blog, we will finally give an answer to THE question: R, Python, Scala, Spark, Tensorflow, etc... What is the best one to answer data science questions? The question itself is totally absurd, but they are so many people asking it on social network that we find it worth...

This blog shows how to perform scalable geospatial data analysis in Scala using Geotrellis, Apache Spark, Sparkling-Water and the Spark-Notebook. As a benchmark for this blog, we use the 500 images (and 45GB) dataset distributed by Kaggle/DSTL. After reading this blog post, you will know how to: Load GeoJSON and...

In this blog, we will see how we can perform geo-spatial data analysis in order to identify new business opportunities. For this showcase, we will focus on the retail sector and more precisely on the super-market leading brands in Belgium: Colruyt, Delhaize, Carrefour and Lidl. We analyzed the location of super-market in Brussels, computed the average time travel to the closest supermarket for Brussels neighborhood and see how these four major brands are sharing their market zone among Brussels neighborhood accordingly. We are reusing the techniques detailed in the Dynamic Web scrapping blog post. The techniques described in this post can be useful for all sorts of B2C companies involved in the retail sector, where competition is generally strong and shop implantation matters.

In this blog, we will see how we can perform in-depth customer analytics using publicly available inputs from the customers on company Facebook pages. For this showcase, we will focus on the media sector and more precisely on the RTL group (leading TV & Radio on the French speaking side...

In this blog, we will see how deep learning techniques (Recurrent Neural Network, RNN and/or Convolutional Neural Network, CNN) can be used to determine the sentiment polarity of a written text. This is call "sentiment analysis" and it's very useful to enhanced the communication with your customers. Such algorithms are...

Scrapping social data from Facebook is of great helps for customer analytics

In a previous blog post, we have seen how to mine information on static web pages. In this blog post, I'll explain how we can do the same on dynamically (i.e. javascript) generated web pages. As a showcase, I will show you how to find the best land investment you...

In this post, I am showing how to scrap movie data from a static web site and the type of questions we can answer from these data.