Fine Tuning

Fine tuning is a machine learning technique where a pre-trained model is further trained on a smaller, domain-specific dataset to adapt it to a particular task or improve its performance. It leverages transfer learning by starting with a model that has already learned general features from a large dataset, then adjusting its parameters to specialize in a new context. This approach is widely used in natural language processing, computer vision, and other AI domains to reduce training time and data requirements.

Also known as: Transfer Learning Fine-Tuning, Model Adaptation, Pre-trained Model Tuning, FT, Fine-Tuning

Why learn Fine Tuning?

Developers should use fine tuning when they have a limited amount of labeled data for a specific task, such as custom text classification, image recognition for niche objects, or adapting language models to specialized domains like legal or medical texts. It is particularly valuable for achieving high accuracy with less computational resources compared to training a model from scratch, making it essential for real-world applications where data is scarce or expensive to collect.

Learning Resources

• tutorial: Hugging Face Fine-Tuning Tutorial
• tutorial: TensorFlow Guide to Fine-Tuning
• course: Coursera: Fine-Tuning Large Language Models
• tutorial: PyTorch Transfer Learning Tutorial
• docs: Fine-Tuning in Machine Learning - Towards Data Science Article