Transfer learning is a machine learning technique where a model developed for one task is reused as the starting point for a model on a second task. The idea is to take a pre-trained model on a related task and fine-tune it on the new task. This can significantly reduce the amount of training data required for the new task.
Key Points
- Pre-trained Models: These are models that have been trained on a large dataset and have learned general features that can be useful for other tasks.
- Fine-tuning: This involves adjusting the weights of the pre-trained model to better fit the new task.
- Applications: Transfer learning is widely used in computer vision, natural language processing, and other fields.
Benefits
- Reduced Data Requirement: With transfer learning, you can achieve good performance on new tasks with less data.
- Faster Training: Fine-tuning a pre-trained model is faster than training a model from scratch.
- Improved Performance: Pre-trained models often have a good baseline performance, which can be improved with fine-tuning.
Example
Let's say you want to build a model to classify images of cats and dogs. Instead of training a model from scratch, you can use a pre-trained model that has been trained on a large dataset of diverse images. You can then fine-tune this model on your specific task of classifying cats and dogs.
Cats and Dogs