Vladimir Zakharov explains how DataFrames serve as a vital tool for data-oriented programming in the Java ecosystem. By ...

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 ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Multiplication in Python may seem simple at first—just use the * operator—but it actually covers far more than just numbers. You can use * to multiply integers and floats, repeat strings and lists, or ...

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 ...

Abstract: Structured sparsity has been proposed as an efficient way to prune the complexity of Machine Learning (ML) applications and to simplify the handling of sparse data in hardware. Accelerating ...

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.