Time series are wonderful. I love them. They are everywhere. And we even get to brag about being able to predict the future! As a follow-up to the article on predicting multiple time-series, I receive lots of messages asking about prediction for more than a single step. A step can be any period of time: a day, a week, a minute, an year… So this is called multi-step forecasting. I want to show you how to do it with neural networks

10. Modern Recurrent Neural Networks navigate_next 10.3. Deep Recurrent Neural Networks search Quick search code Show Source Table Of Contents 1. Introduction 2. Preliminaries 2.1. Data Manipulation 2.2. Data Preprocessing 2.3. Linear Algebra 2.4. Calculus 2.5. Automatic Differentiation 2.6. Probability and Statistics 2.7. Documentation 3. Linear Neural Networks for Regression 3.1. Linear Regression 3.2. Object-Oriented Design for Implementation 3.3. Synthetic Regression Data 3.4. Linear Regression Implemen

A model that pays attention to your graph

Abstract page for arXiv paper 2605.31315: Graph Neural Networks Are Not Continuous Across Graph Resolutions

The human brain, with its remarkable general intelligence and exceptional efficiency in power consumption, serves as a constant inspiration and aspiration for the field of artificial intelligence. Drawing insights from the ...

Research on Deep Neural Networks (DNNs) has focused on improving performance and accuracy for real-world deployments, leading to new models, such as Spiking Neural Networks (SNNs), and optimization techniques, e.g., quantization and pruning for compressed networks. However, the deployment of these innovative models and optimization techniques introduces possible reliability issues, which is a pillar for DNNs to be widely used in safety-critical applications, e.g., autonomous driving. Moreover, scaling techn

nan loss, why did it happen, what went wrong?

Introduction to Deep Learning Neural Networks

A short reference on the neural-network vocabulary used across the neural texture, neural material and neural appearance posts — MLP, weights and biases, ReLU, forward pass, loss, gradient descent, backpropagation, Adam, latent vectors, feature grids and decoder networks — explained once, concretely, so those posts can link here instead of re-deriving it

Sid Black*, Lee Sharkey*, Leo Grinsztajn, Eric Winsor, Dan Braun, Jacob Merizian, Kip Parker, Carlos Ramón Guevara, Beren Millidge, Gabriel Alfour, C…