Embeddings vs. Fine-Tuning

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.