Comprehensive guide to data augmentation techniques for training robust deep learning models. Why Data Augmentation? Benefits: Increases training data diversity Reduces overfitting Improves model generalization Makes models robust to variations Cost-effective alternative to collecting more data Common Use Cases: …

Image embeddings convert visual content into dense vector representations that capture semantic and visual features, enabling similarity search, classification, and retrieval. Core Idea Image embeddings map images to fixed-size vectors in a high-dimensional space where semantically similar images are close together. …