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.
NLPCloud.io 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 …
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.
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.
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.