From a neuroscience perspective, artificial neural networks are regarded as abstract models of biological neurons, yet they rely on biologically implausible backpropagation for training. Energy-based ...

Over the past decade, deep learning (DL) techniques such as convolutional neural networks (CNNs) and long short-term memory (LSTM) networks have played a pivotal role in advancing the field of ...

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 ...

The choice between PyTorch and TensorFlow remains one of the most debated decisions in AI development. Both frameworks have evolved dramatically since their inception, converging in some areas while ...

Deep neural networks (DNNs), the machine learning algorithms underpinning the functioning of large language models (LLMs) and other artificial intelligence (AI) models, learn to make accurate ...

The Kentucky Council on Postsecondary Education (CPE), in partnership with the Department for Community-Based Services (DCBS) and a consortium of postsecondary institutions, kicked off the Community ...

“Prevention Is Better Than Cure” – The Dutch philosopher Desiderius Erasmus. In this project, we aim to develop an application that can help to detect the disease of poultry via poultry birds by deep ...

Abstract: Recently, deep neural network-based methods have been proposed for vehicle behavior prediction (VBP). However, existing methods lack an enhanced discriminative mechanism and robust learning ...

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A team of astronomers led by Michael Janssen (Radboud University, The Netherlands) has trained a neural network with millions of synthetic black hole data sets. Based on the network and data from the ...