Process Diverse Data Types at Scale: Through the Unstructured partnership, organizations can automatically parse and transform documents, PDFs, images, and audio into high-quality embeddings at ...

Here’s a quick library to write your GPU-based operators and execute them in your Nvidia, AMD, Intel or whatever, along with my new VisualDML tool to design your operators visually. This is a follow ...

NVIDIA releases detailed cuTile Python tutorial for Blackwell GPUs, demonstrating matrix multiplication achieving over 90% of cuBLAS performance with simplified code. NVIDIA has published a ...

Visualize free body diagrams using vector math in Python to better understand forces and motion. This video shows how vectors represent forces, how they combine mathematically, and how Python helps ...

Abstract: Emerging applications, e.g., machine learning, large language models (LLMs), and graphic processing, are rapidly developing and are both compute-intensive and memory-intensive. Computing in ...

Researchers create a photochromic fluorescent system that performs optical neural computing and visual output in one step, cutting power use and complexity. (Nanowerk News) The rapid growth of ...

Element-wise multiplication in Python is a fundamental operation, especially when working with numerical data using libraries like NumPy. Understanding how to perform this efficiently is crucial for ...

The growing imbalance between the amount of data that needs to be processed to train large language models (LLMs) and the inability to move that data back and forth fast enough between memories and ...

A new technical paper titled “Leveraging ASIC AI Chips for Homomorphic Encryption” was published by researchers at Georgia Tech, MIT, Google and Cornell University. “Cloud-based services are making ...

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.