Example Concept: Embedding models transform images into lower-dimensional vectors that capture essential features. By using pre-trained models (transfer learning), you can start with a model already adept at recognizing general patterns. Fine-tuning involves adjusting the model's layers to better fit your specific dataset, while data augmentation increases the diversity of your training data by applying transformations like rotation and flipping.