Step-by-step guide to building a neural network entirely from scratch in Java. Perfect for learning the fundamentals of deep learning. #NeuralNetwork #JavaProgramming #DeepLearning Mike Johnson gives ...

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 ...

Abstract: This advanced tutorial explores some recent applications of artificial neural networks (ANNs) to stochastic discrete-event simulation (DES). We first review some basic concepts and then give ...

Artificial intelligence might now be solving advanced math, performing complex reasoning, and even using personal computers, but today’s algorithms could still learn a thing or two from microscopic ...

Abstract: Activation functions are pivotal in neural networks, determining the output of each neuron. Traditionally, functions like sigmoid and ReLU have been static and deterministic. However, the ...

“Neural networks are currently the most powerful tools in artificial intelligence,” said Sebastian Wetzel, a researcher at the Perimeter Institute for Theoretical Physics. “When we scale them up to ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.

Principal Research Fellow at AI and Cyber Futures Institute, Charles Sturt University Optical illusions, quantum mechanics and neural networks might seem to be quite unrelated topics at first glance.

ABSTRACT: Internet of Things (IoT) networks present unique cybersecurity challenges due to their distributed and heterogeneous nature. Our study explores the effectiveness of two types of deep ...