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, ...

Deep learning neural networks, exemplified by models like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Generative Adversarial Networks (GANs), have achieved remarkable ...

The information bottleneck (IB) principle is a powerful information‐theoretic framework that seeks to compress data representations while preserving the information most pertinent to a given task.

Machine Learning (ML) and Deep Learning (DL) are branches of Artificial Intelligence (AI). ML helps computers learn from data ...

The recently published book Understanding Deep Learning by [Simon J. D. Prince] is notable not only for focusing primarily on the concepts behind Deep Learning — which should make it highly accessible ...

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.

Melbourne, Australia - 12 August 2025 - Researchers have demonstrated that brain cells learn faster and carry out complex networking more effectively than machine learning by comparing how both a ...

Join the event trusted by enterprise leaders for nearly two decades. VB Transform brings together the people building real enterprise AI strategy. Learn more In the current artificial intelligence (AI ...

Learn how the Inception Net V1 architecture works and how to implement it from scratch using PyTorch. Perfect for deep ...

Blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) is a cornerstone of non-invasive brain function investigation, yet its ...

The initial research papers date back to 2018, but for most, the notion of liquid networks (or liquid neural networks) is a new one. It was “Liquid Time-constant Networks,” published at the tail end ...