Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...

The textbook meaning of an artificial neural network (ANN) is a deep learning model made up of neurons that emulate the structure of the human brain. These neurons are designed to mimic the way nerve ...

Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...

A critical procedure in diagnosing atrial fibrillation is the creation of electro-anatomic activation maps. Current methods generate these mappings from interpolation using a few sparse data points ...

Western researchers have developed a novel technique using math to understand exactly how neural networks make decisions – a widely recognized but poorly understood process in the field of machine ...

This book is an introduction to machine learning, with a strong focus on the mathematics behind the standard algorithms and techniques in ... kernel methods, Gaussian processes, deep neural networks, ...

Spiking Neural Networks (SNNs) are a pathway that could potentially empower low-power event-driven neuromorphic hardware due to their spatio-temporal information ...

Abstract: As we saw in the previous chapter, machine learning comprises three components: the underlying model, the cost function, and a method of modifying the parameters of the model. In this ...