ReLU, short for Rectified Linear Unit, is an activation function that outputs the input if it is positive and zero otherwise. Its formula is f(x) = max(0, x).

Today we're joined by Alona Fyshe, an assistant professor at the University of Alberta. We caught up with Alona on the heels of an interesting panel discussion that she...

The success of Graph Neural Networks (GNN) in learning on non-Euclidean data arouses many subtopics, such as Label-inputted GNN (LGNN) and Implicit GNN (IGNN). LGNN, explicitly inputting supervising information (a.k.a. labels) in GNN, integrates label propagation to achieve superior performance, but with the dilemma between its propagating distance and adaptiveness. IGNN, outputting an equilibrium point by iterating its network infinite times, exploits information in the entire graph to capture long-range d

And why is this a very important step in understanding why they work?

Although the temporal spike dynamics of spiking neural networks (SNNs) enable low-power temporal capture capabilities, they also incur inherent inconsistencies that

Abstract page for arXiv paper 1406.3284: Deep Neural Networks Rival the Representation of Primate IT Cortex for Core Visual Object Recognition

Categories Blog Case Studies Documents eBooks News and Events Reference Architecture Supported Software Whitepapers Return to Homepage Artificial Intelligence 5 Types of LSTM Recurrent Neural Networks December 28, 2023 14 min read Before LSTMs - Recurrent Neural Networks Utilizing past experiences to enhance future performance is a key aspect of deep learning, as well as machine learning in general. In neural networks, performance improvement through experience is encoded by model parameters called weights

Skip to content SudoAll About LSTM Networks Explained Posted on March 18, 2025March 3, 2026 by David Saliba Introduction: The Power of LSTMs The Long Short-Term Memory (LSTM) network is a specialised kind of Recurrent Neural Network (RNN) architecture, designed specifically to solve the problem of vanishing gradients that plagues traditional RNNs when dealing with long sequences of data. While standard RNNs struggle to retain information from many steps ago, LSTMs are engineered with a dedicated Cell State

Siamese network is an artificial neural network that is used to find out how similar two objects are when comapring them with each other

Explaining what an artificial neural network is, how it works, and what it does through its major characteristics