Neural

Staying on top of a fast-growing research field is rarely easy. I face this challenge firsthand as a practitioner in Physics-Informed Neural Networks (PINNs). Latest papers, be they algorithmic advancements or cutting-edge applications, are published...

Just as GPUs once eclipsed CPUs for AI workloads, Neural Processing Units (NPUs) are set to challenge GPUs by delivering even faster, more efficient performance—especially for generative AI, where massive real-time processing must occur...

A basic understanding of NeRF’s workings through visual representationsWho should read this text?This text goals to supply a basic beginner level understanding of NeRF’s workings through visual representations. While various blogs offer detailed explanations...

A deep dive into the world of computational modeling and its applicationsFor a long time, scientists have sought to grasp how humans make decisions — whether we’re selecting what to eat for lunch or...

When there are more features than model dimensionsIt could be ideal if the world of neural network represented a one-to-one relationship: each neuron prompts on one and just one feature. In such a world,...

Once the network has been trained, though, things get way, way cheaper. Petersen compared his logic-gate networks with a cohort of other ultra-efficient networks, akin to binary neural networks, which use simplified perceptrons that...

The e-commerce industry has seen remarkable progress over the past decade, with 3D rendering technologies revolutionizing how customers interact with products online. Static 2D images are not any longer enough to capture the eye...

A simple step-by-step guide to getting began with Neural Networks for Time Series ForecastingForecasting multiple time series can quickly develop into an advanced task; traditional approaches either require a separate model per series (i.e....