An interactive introduction to spiking neuron models -- all in the browser. Learn about modeling neurons as dynamical systems and the computational properties of neurons.

Self-supervised learning (SSL) trains neural networks by generating labels directly from the input data instead of relyi

Lehrstuhl fuer Integrierte Digitale Systeme und Schaltungsentwurf, RWTH Aachen

I’ve trained neural networks to generate candy hearts before, and the process goes something like this: collect as many existing candy heart messages as I can (which was 366) give them to a clueless neural net that tries to imitate them allow the neural net to generate its 100% humanlike imitations

Today we want to build a model, that can identify ingredients in cooking recipes. I use the “German Recipes Dataset”, I recently published on kaggle. We have more than 12000 German recipes and their ingredients list.

What is Recurrent Neural Network? A Recurrent Neural Network (RNN) is a type of neural network designed for processing sequences of data. Learn more in the SEOFAI AI Glossary

Temporal Graph Neural Networks (TGNNs) aim to capture the evolving structure and timing of interactions in dynamic graphs. Although many models incorporate time through

Abstract page for arXiv paper 2507.15205: Long-Short Distance Graph Neural Networks and Improved Curriculum Learning for Emotion Recognition in Conversation

Blog Topics Advertise Join Newsletter Building Convolutional Neural Network using NumPy from Scratch In this article, CNN is created using only NumPy library. Just three layers are created which are convolution (conv for short), ReLU, and max pooling. By Ahmed Gad , KDnuggets Contributor on April 26, 2018 in Convolutional Neural Networks , Image Recognition , Neural Networks , numpy , Python --> comments Using already existing models in ML/DL libraries might be helpful in some cases. But to have better cont

From the statistical learning perspective, complexity control via explicit regularization is a necessity for improving the generalization of over-parameterized models. However, the impressive generalization performance of neural networks with only implicit regularization may be at odds with this conventional wisdom. In this work, we revisit the importance of explicit regularization for obtaining well-calibrated predictive uncertainty. Specifically, we introduce a probabilistic measure of calibration perform