A Graph Neural Networks (GNN) is a class of artificial neural networks for processing graph data. Here we need to define what a graph is, and a definition is a quite simple – a graph is a set of vertices (nodes) and a set of edges representing the connections between the vertices. There are many data sources which produce data that can be organically presented in a graph form. For example, we can consider social network users as graph vertices where two vertices are connected if corresponding users are friends.Read More
In this article, I want to talk about the use of convolutional neural networks for the classification of images by style.
The goal of our project is to build software to identify whether an image is in the “BMW style”. In other words, we are faced with the task of classifying images. It is important to note here that images could be of any content, with and without cars. So, the main interest here is not to identify a car object, or identify a BMW car, rather identify a BMW look and feel – colors, composition and so on. But we can’t select these attributes of style manually. To solve this problem, it was proposed to use a neural network, in which such complex features will be found automatically in the learning process.
In this article we will explain what a recurrent neural network is and study some recurrent models, including the most popular LSTM model. After the theoretical part we will write a complete simple example of recurrent network in Python 3 using Keras and Tensorflow libraries, which you can use as a playground for your experiments.