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 ...

As a result, the on-chip learning-based neuromorphic system achieved up to 20,000 times faster processing speed while maintaining similar interpretation accuracy to existing conventional techniques.

Get started with Java streams, including how to create streams from Java collections, the mechanics of a stream pipeline, examples of functional programming with Java streams, and more. You can think ...

Learn how Network in Network (NiN) architectures work and how to implement them using PyTorch. This tutorial covers the concept, benefits, and step-by-step coding examples to help you build better ...

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 Secret Service ...

ABSTRACT: This paper investigates the application of machine learning techniques to optimize complex spray-drying operations in manufacturing environments. Using a mixed-methods approach that combines ...

Spiking neural networks (SNNs), which are the next generation of artificial neural networks (ANNs), offer a closer mimicry to natural neural networks and hold promise for significant improvements in ...

Binary neural network with 0/1 invert weights. Trained with evolutionary reinforcement algorithm, at various cycle counts. Swapped memory array is filled with data at inputs and zeros otherwise.

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 ...