Convolutional Neural Networks (CNNs) were originally designed for deep learning computer vision tasks, but they have proven highly useful for Natural Language Processing (NLP) tasks as well. A primary use case is sentence...

A Graph Neural Network, also known as a Graph Convolutional Network (GCN), is an image classification method. In this article, we’ll provide an introduction to the concepts of graphs, convolutional neural networks,...

There are several types of Convolutional Neural Networks (CNNs) being developed and all have the potential to greatly contribute to the speed and accuracy of automatic image identification. In particular, 3D CNNs...

There are many effective ways to automatically classify entities. In this article, we cover six common classification algorithms, of which neural networks are just one choice. Which algorithm is the best choice...

Backpropagation is a basic concept in modern neural network training. In real-world projects, you will not perform backpropagation yourself, as it is computed out of the box by deep learning frameworks and...

Many of the companies rely on image segmentation techniques powered by Convolutional Neural Networks (CNNs), which form the basis of deep learning for computer vision. Image segmentation involves drawing the boundaries of the...

Neural network activation functions are a crucial component of deep learning. Activation functions determine the output of a deep learning model, its accuracy, and also the computational efficiency of training a model—which...

Neural networks are mathematical constructs that generate predictions for complex problems. The basic unit of a neural network is a neuron, and each neuron serves a specific function. Bias serves two functions...

Image recognition uses artificial intelligence technology to automatically identify objects, people, places and actions in images