Data augmentation is a technique used in machine learning to increase the diversity of training data without collecting new data, thereby improving model generalization and performance. It involves applying various transformations to existing data samples, such as rotation, scaling, and flipping, to create new, synthetic examples that help the model learn more robustly.

Data Augmentation

Introduction to Generative AI: Understanding the Basics and Key Technologies

Understanding the Process of Training a YOLO Model

Building Neural Networks for Image Recognition

Creating a Custom YOLO Model from Scratch in C#

Implement YOLO with Generative Adversarial Networks in C#

Continuous Improvement in Machine Learning

ImageNet Challenge Revolution

Overfitting Avoidance

Data Engineering and Preprocessing Techniques for AI Systems

Random Cropping

Relevant Degrees