Information Theory Meets Deep Neural Networks: Theory and Applications. The previous volume can be viewed here: Volume I Deep Neural Networks (DNNs) have become one of the most popular research ...

Overview Books provide a deeper understanding of AI concepts beyond running code or tutorials.Hands-on examples and practical ...

This repository contains a Monte-Carlo solver to train neural-network variational wavefunction to solve continuous-space Fermi systems [M Geier, K Nazaryan, T Zaklama, L Fu, Phys. Rev. B 112, 045119 ...

Deep learning has added a new dimension to engineering applications, from 5G signal processing to predictive maintenance in power grids. It automatically detects equipment failures and optimizes ...

Abstract: Road network information is a core component of online maps and plays a crucial role in navigation, urban planning, and traffic management. Convolutional neural networks (CNNs) have ...

We will create a Deep Neural Network python from scratch. We are not going to use Tensorflow or any built-in model to write the code, but it's entirely from scratch in python. We will code Deep Neural ...

Cornell University researchers have developed a low-power microchip they call a "microwave brain," the first processor to compute on both ultrafast data signals and wireless communication signals by ...

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

Johns Hopkins University researchers have grown a novel whole-brain organoid, complete with neural tissues and rudimentary blood vessels—an advance that could usher in a new era of research into ...

Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the ...