Bibek Bhattarai details Intel's AMX, highlighting its role in accelerating deep learning on CPUs. He explains how AMX ...

Swaminathan Sethuraman, a data engineer, bridges AI theory and practice with research on continuous learning and neural ...

An alternative to manual design is “neural architecture search” (NAS), a series of machine learning techniques that can help discover optimal neural networks for a given problem.

We dive into Transformers in Deep Learning, a revolutionary architecture that powers today's cutting-edge models like GPT and BERT. We’ll break down the core concepts behind attention mechanisms, self ...

Neural architecture search is an aspect of AutoML, along with feature engineering, transfer learning, and hyperparameter optimization. It’s probably the hardest machine learning problem ...

An academic study by Goodfellow et al. in 2014 reinvigorated the interest in deepfakes through a new deep learning architecture called Generative Adversarial Networks (GANs).

This is the gap that machine learning, and specifically deep learning, fills.” Thanks to MCUNetV2 and other advances in TinyML, Warden’s forecast is fast turning into a reality.

There’s a big problem faced by many organizations that are trying to unlock the promise of machine learning and artificial intelligence. The process of building and using machine learning architecture ...

A new computing architecture enables advanced machine-learning computations to be performed on a low-power, memory-constrained edge device. The technique may enable self-driving cars to make ...

A conversation with Randy Bass on his chapter in Recentering Learning. This conversation is with the author of the chapter “Architecture of the Unexpected: Beyond the Learning Paradigm” in our new ...