- Installation - 🚀 Quick Start - PyNeuraLogic Language - Problem Definition - Understanding Rules - Model Evaluation - 🦓 Module Zoo - Advanced Usage - 🔬 Examples - ⏱️ Benchmarks - Hypergraph Neural Networks Heterophily Settings - Hypergraph Neural Networks - View page source # Hypergraph Neural Networks A hypergraph is a generalization of a simple graph \(G = (V, E)\), where \(V\) is a set of vertices and \(E\) is a set of edges (hyperedges) connecting an arbitrary number of vertices
Spiking Neural Networks (SNN) represent the third generation of artificial neural networks. SNN models are built using both spatial and temporal aspects of the input data, and as such advance a step closer to true brain-inspired processing. SNNs have shown great promise and potential in low-power sensory-processing and edge computing hardware platforms. In the paper
I left the field when neural networks were a failed research program. I came back to find they had won everything. Part 1 of a catch-up, two decades deep
Hironobu SUZUKI @ InterDB > Part 1: Neural Networks Part 1: Neural Networks --> This part delves into the fundamental concepts of neural networks. While these techniques and ideas emerged in the previous century, they remain foundational for understanding contemporary AI technologies. Part Contents Convolutional Neural Networks (CNNs) are not covered in this document as they are primarily used for image and video processing. The Engineer's Guide To Deep Learning Search Home Part 1: Neural Networks 1. Percep
CHAPTER 4 A visual proof that neural nets can compute any function Neural Networks and Deep Learning What this book is about On the exercises and problems Using neural nets to recognize handwritten digits Perceptrons Sigmoid neurons The architecture of neural networks A simple network to classify handwritten digits Learning with gradient descent Implementing our network to classify digits Toward deep learning How the backpropagation algorithm works Warm up: a fast matrix-based approach to computing the outp
Learn how neural networks with one hidden layer using ReLU activation represent continuous nonlinear functions
Neural network normalization and scaling explained: Learn how these techniques can speed up learning by optimizing the error surface for faster convergence
Search # Tiled convolutional neural networks Jiquan Ngiam, Zhenghao Chen, Daniel Chia, Pang W. Koh, Quoc V. Le, Andrew Y. Ng Advances in Neural Information Processing Systems 23 (NIPS 2010) ## Abstract Convolutional neural networks (CNNs) have been successfully applied to many tasks such as digit and object recognition. Using convolutional (tied) weights significantly reduces the number of parameters that have to be learned, and also allows translational invariance to be hard-coded into the architecture
Explore learning rates in neural networks, including what they are, different types, and machine learning applications where you can see them in action
This explores how to build neural networks whose inner workings are legible from the start — through architecture and training choices — rather than reverse-engineering them after the fact