Summary

This chapter introduced CLIP, a powerful DL model designed for cross-modal tasks, such as finding relevant images based on textual queries or vice versa. We learned that the model’s dual encoder architecture and contrastive learning mechanism enable it to understand both images and text in a shared space.

We implemented our customized versions of CLIP models, using the DistilBERT and ResNet50 models. Following an exploration of the Flickr8k dataset, we built a CLIP model and explored its capabilities in text-to-image and image-to-image searches. CLIP excels at zero-shot transfer learning. We showcased this by using a pre-trained CLIP model for image search and CIFAR-100 classification.

In the next chapter, we will focus on the third type of machine learning problem: reinforcement learning. You will learn how the reinforcement learning model learns by interacting with the environment to reach its learning goal.