Graph neural networks (GNNs) have emerged as a powerful framework for analyzing and learning from structured data represented as graphs. GNNs operate directly on graphs, as opposed to conventional ...

Liquid Neural Networks could help us to achieve the next level of efficiency with AI/ML Many of us can agree that over the past few years AI/ML progress has been, well, rapid. Now, we’re given yet ...

Learn what CNN is in deep learning, how they work, and why they power modern image recognition AI and computer vision ...

Every day, various types of sensory information fromthe external environment are transferred to the brainthrough different modalities and then processed to generate a series of coping behaviors. Among ...

Each algorithm is built for a different type of problem, and they all engage in subtly different kinds of machine learning. Here, we'll discuss four major subtypes of software neural networks: ...

Binary digits and circuit patterns forming a silhouette of a head. Neural networks and deep learning are closely related artificial intelligence technologies. While they are often used in tandem, ...

Peter van der Made is the founder and CTO of BrainChip Ltd. BrainChip produces advanced AI processors in digital neuromorphic technologies. The artificial intelligence (AI) revolution is upon us, and ...

The case for building Scalable Neuromorphic Networks is this: like humans, smarter chips have a larger, tighter neural network. Indeed, neural networks are the current state-of-the-art for machine ...

What are convolutional neural networks in deep learning? Convolutional neural networks are used in computer vision tasks, which employ convolutional layers to extract features from input data.

As the name suggests, neural networks are inspired by the brain. A neural network is designed to mimic how our brains work to recognize complex patterns and improve over time. Neural networks train ...