In this video, we will understand forward propagation and backward propagation. Forward propagation and backward propagation ...

We have explained the difference between Deep Learning and Machine Learning in simple language with practical use cases.

Regularization in Deep Learning is very important to overcome overfitting. When your training accuracy is very high, but test ...

Even networks long considered "untrainable" can learn effectively with a bit of a helping hand. Researchers at MIT's Computer ...

Learn about the most prominent types of modern neural networks such as feedforward, recurrent, convolutional, and transformer networks, and their use cases in modern AI. Neural networks are 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 ...

AI methods are increasingly being used to improve grid reliability. Physics-informed neural networks are highlighted as a ...

The TLE-PINN method integrates EPINN and deep learning models through a transfer learning framework, combining strong physical constraints and efficient computational capabilities to accurately ...

As artificial intelligence explodes in popularity, two of its pioneers have nabbed the 2024 Nobel Prize in physics. The prize surprised many, as these developments are typically associated with ...

Neural Concept aims to accelerate these timelines by integrating AI directly into CAD and physics-based simulation ...