In this course, we’ll examine the history of neural networks and state-of-the-art approaches to deep learning. Students will learn to design neural network architectures and training procedures via ...

One of the main challenges of neural networks and deep learning is the need for large amounts of data and computational resources. Neural networks learn from data by adjusting their parameters to ...

Deep neural networks (DNNs) have revolutionized machine learning, yet their success defies classical expectations. They ...

Computer vision is used in many sectors for its ability to monitor and analyze visual data in ways that extend past what ...

AI refers to any machine that is able to replicate human cognitive skills, such as problem solving. Over the second half of the 20th century, machine learning emerged as a powerful AI approach that ...

Researchers at Oxford University uncover how deep neural networks possess an intrinsic simplicity bias, enabling them to ...

Malicious URLs are among the most common vectors for cyberattacks, enabling phishing, malware distribution, and data theft.

Researchers have developed a transfer learning-enhanced physics-informed neural network (TLE-PINN) for predicting melt pool morphology in selective laser melting (SLM). This novel approach combines ...

This article establishes a neural network-based technique for automatic peak picking in 2D NMR spectroscopy, demonstrating a ...

Researchers have explored the potential of deep neural networks (DNNs) in transforming fragrance design. By analyzing the sensing data of 180 essential oils, the DNN was trained using the odor ...