Embeddings vs. Fine-Tuning
Explore the key differences between embeddings and fine-tuning in vector databases. Understand how embeddings represent data semantically and how fine-tuning adapts pretrained models to specific tasks. Learn the process of fine-tuning the CLIP model on custom image-text datasets and how it can improve AI applications.
We have learned enough about embeddings in the previous lessons. Let’s recap what we have studied so far.
Embeddings are dense vector representations of data, capturing the semantic meaning and making it easier to work with different data types such as text, images, videos, audio, etc. Embeddings are more often generated using pretrained machine learning models. In the previous lessons, we explored generating embeddings for different types of data using pretrained models. Now, we will focus on fine-tuning, a technique used to adapt pretrained models to perform better on specific tasks with custom datasets.
What is fine-tuning?
Fine-tuning involves taking a pretrained model and retraining it on a smaller, task-specific dataset to improve its performance for that particular task. This process allows the model to learn the nuances and patterns specific to the new data, enhancing its accuracy and effectiveness.
Fine-tuning is particularly useful when we have a ...