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Generate realistic test data in Python fast. No dataset required

Learn the NumPy trick for generating synthetic data that actually behaves like real data.
The Lancet

CARDBiomedBench: a benchmark for evaluating the performance of large language models in biomedical research

Although large language models (LLMs) have the potential to transform biomedical research, their ability to reason accurately across complex, data-rich domains remains unproven. To address this ...
IEEE

Implimentation and evaluation of an efficient clock distribution network for deep-submicron technology

Abstract: A differential clock distribution network using current mode logic (CML) buffer and RCL interconnect model for low-skew is presented in this paper. We investigate attenuation and skew of the ...
GitHub

This VL-JEPA implimentation takes direct insperation from the original VL-JEPA paper

The goal is to train a good predictor for the two pretrained models (DINOv3 & EmbeddingGemma) I assume the pretrained models already have a unique representation of the world Each model has a ...
GitHub

Engineer1999/A-Curated-List-of-Machine-Learning-Projects-With-Code

This repository contains a curated collection of 500+ Machine Learing Projects with code, detailing practical applications and insights into machine learning (ML) implimentation. The contents are ...