Tag archives: python

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 criticism 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 perform efficient reverse image search.

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 to finally answer the recurrent question using a scientific methodology.  At the end of this blog, you will find a quantitative answer comparing the computing time of each language/library for fitting the exact same Generalized Linear Model (GLM).  Many features matter in the choice of a language/library, among them , the computing and developing time are for sure very important criteria.

In this blog, we will see how we can perform geospatial data analysis in order to identify new business opportunities.  For this showcase, we will focus on the retail sector and more precisely on the supermarket leading brands in Belgium: Colruyt, Delhaize, Carrefour, and Lidl.  We analyzed the location of supermarkets 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 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 typically used to analyze emails, website or even Facebook posts where your customers may talk about your products. Thanks to this, you can prioritize your answers and react faster to the unsatisfied customers... ...

Nowadays, social networks can be considered as a main source of data.  This is particularly true for business to customer companies which must take into account customer feedback on their products.  In this blog, we will show how to retrieve information from Facebook using the Facebook Graph API... ...

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 can make in Belgium today... ...

Every data science journey starts by aggregating the data of interest.  In the industry sector, those are often coming directly from sensors, user surveys, software or application used by your customers.  Nonetheless, the information publicly available on the web still remain an important source of additional information like news, weather or even geographical addresses.  Today, we will focus on movie data... ...