Data Management

Data Access


  • Objects detection using YOLO v4 - We will see in this article, how with the YOLO neural network we can very simply detect several objects in a photo. The objective is not to go into the details of the implementation of this neural network (much more complex than a simple sequential CNN) but rather to show how to use the implementation which was carried out in C ++ and which is called Darknet.
  • Transfer Learning with VGG - In this article we will discuss the concept of Transfer Learning ... or how to avoid redoing long and consuming learning by partially reusing a pre-trained neural network. To do this we will use a network which is the reference in the matter: VGG-Net (vgg16).
  • Image processing (part 7) Convolution Neural Networks – CNN - In this article we will see step by step how to create and use a convolutional neural network (CNN) to classify images.
  • Image processing (part 6) Filters & Convolution - We will discuss in this post a kind of filters widely used by all images software (such as Photoshop or Gimp). In fact and to go further (without "sploiling" the following posts either) this convolution principle will also be widely used by neural networks (Deep Learning) ... but we will see that later. First of all, let's focus on the principle of these convolution filters.
  • Image processing (part 5) Morphologic Transformations - We will see in this article the principles of erosion and dilation of images which are widely used especially during the restoration of poor quality images.
  • Image processing (part 4) Image Transformations - We will see in this article how to perform some basic transformations on images with scikit-image such as rotating, and changing image scale and size.
  • Image processing (part 3) Image Thresholding - In this article (which is the 3rd episode of the image processing series) we will see how to use the image histograms we discussed in article 2 to do some basic editing.
  • Image processing (part 2) the histograms - In the previous article we saw how our digital images were built and stored. This naturally brings us to the image histograms. Of course we don’t manage an image like we do for a text . Images are in fact just matrix (like a pixel map ), so first of all we need to analyse the image, and to do that we'll take a look on the pixel histograms.
  • Image processing (part 1) the digital representation - In this article we will see and especially understand how images are stored in a computer just to make it usable by other softwares. In fact, this post is the first within a series that will allow us to approach image processing in general but also subsequently the place of Artificial Intelligence and especially Deep Learning in this discipline which is part of a set known as computer vision.
  • Automate image straightening with Blue Prism and Python - In this article we will see how to automate image straightening with blue Prism and Python.


  • Managing location data - In this article, learn how to easily use, cleanse, and visualize geo-localized data with Python.

Texts & Strings

  • Introduction to - is an API for easily using NLP in production. The API is based on the pre-trained models of spaCy and Hugging Face (based on transformers). In and article we will see how to use this API in a few lines ...
  • Fake News ? - I propose in this article to create a convolutional neural network to do NLP, and for the data I will use a dataset that you can simply find in the Kaggle datasets: FrenchFakeNewsDetector. You have understood the objective is twofold: on the one hand to see how we can use the convolution technique with vectors (1 dimension instead of images with 2+ dimensions) and on the other hand to do NLP with data in French.
  • Retrieve historical meteo data - For an analysis I wanted to do and after several searches, I realized that it was not that easy to get historical weather data. Of course, as i'm french I went to Meteo France Open Data and tried other open data site. But nothing really usable or it seems without a paid subscription. So I decided to retrieve them through a Python program and the scraping technique.
  • Getting data from a pdf using Python - In this article you will learn how to easily recover data from a pdf file using Python.
  • Sentiment analysis on movie reviews - The purpose of this article is to show through a concrete and French case the method to perform a sentiment analysis with Python.
  • KYC check with Blue Prism Decipher IDP - Discover in a short demo how Blur Prism Decipher IDP allows you to easily automate the classification and then the extraction of data from unstructured documents (scanned for example).
  • NLP with Python NLTK - Find out in this article how to do NLP simply with Python and NLTK.
  • Covid-19: Recover French official raw data - Retrieve data from Public Health France and the Ministry of Health on Covid-19 and with Python.
  • Strings Comparison - Find out in this article how to use distance algorithms and the Fuzzywuzzy library to compare strings.
  • Advanced use of Tesseract with Python - Find out in this article how to use tesseract in relatively complex cases with Python and OpenCV.



  • Speech recognition with Python & Google - In this article, which is above all practical, we will see how with your computer and Python to capture vocal information and transcribe it into text. Want to create your own digital assistant? why not, here's how to start it anyway ...

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