In this short tuto, we will discover the Plotly data visualization library and we’ll pratice this library through hands on
In my previous articles on YOLO we saw how to use this network … but when we apply this algorithm on complex images we quickly see that multiple detections are made for the same objects. We will see in this article how to remove these duplicate frames with the so-called NMS technique.
Who has never heard about “Citizen development”? in this short post I am trying to counteract this trend by positioning above the current limits and also figuring out the next coming steps. Just ask yourself if business users really wants to develop their applications or processes? Is this way of doing things just a step towards something much more augmented?
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.
In this tutorial, I invite you to discover a small open source framework that is very easy to set up and use and which will allow you to create an interface for your Machine Learning models. Follow the leader …
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).
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.