StyleGAN is a family of generative adversarial networks introduced by NVIDIA that produces high‑resolution, photorealistic images with unusually fine control over visual attributes such as pose, identity, texture, and lighting.
StyleGAN is a family of generative adversarial networks introduced by NVIDIA that produces high‑resolution, photorealistic images with unusually fine control over visual attributes such as pose, identity, texture, and lighting.
Explainable AI (often shortened to XAI) is a collection of methods and processes that make an AI system’s predictions and behavior understandable to people, so stakeholders can trust, validate, and
Generative Adversarial Networks, commonly called GANs, are a class of deep learning models that learn to generate new, realistic data by pitting two neural networks against each other in a
Ensemble learning is a technique that combines multiple models to produce predictions that are more accurate, robust, and reliable than those from any single model. By aggregating the strengths of
XGBoost, short for eXtreme Gradient Boosting, is a high-performance implementation of gradient-boosted decision trees designed for speed, accuracy, and scalability on structured/tabular data. It builds an ensemble of shallow decision
A linear regression model learns a straight line or hyperplane that best predicts a target from a set of input features. The prediction is a weighted sum of features plus
Swarm intelligence refers to intelligent behavior that emerges from the collective actions of many simple, decentralized agents—whether animals like ants and birds or artificial systems like robots and software agents—without
Machine learning models are powerful tools. Their true value unlocks with deployment. A deployed model makes predictions in the real world. This integration solves actual problems. Modern deployment uses MLOps
Federated learning is a technique for training a shared machine learning model across many devices or organizations while keeping their raw data in place. Instead of sending personal or sensitive
Training a logistic regression model involves teaching it to predict a binary outcome—typically labeled as 0 or 1—based on input features. It’s a foundational algorithm in machine learning, especially useful