Transfer learning, also known as transfer learning, is a machine learning method that allows us to use a model pre-trained on a large dataset to solve a new problem. This method is particularly useful when the amount of labeled data for the new problem is limited.
Transfer Learning Basics
- Pre-trained Models: These are models that have been trained on large datasets, such as ImageNet, and have learned general features from the data.
- Fine-tuning: This process involves taking a pre-trained model and further training it on a smaller dataset specific to the new problem.
- Applications: Transfer learning is widely used in fields such as computer vision, natural language processing, and speech recognition.
Transfer Learning Process
- Data Preparation: Prepare the dataset for the new problem. This may involve data cleaning, augmentation, and normalization.
- Model Selection: Choose a pre-trained model that is suitable for the new problem.
- Fine-tuning: Fine-tune the model on the new dataset.
- Evaluation: Evaluate the performance of the model on the new dataset.
Example
Suppose you want to train a model to recognize cats and dogs. You can use a pre-trained model like ResNet, which has been trained on a large dataset of images. You then fine-tune this model on a smaller dataset of cat and dog images.
## More Information
For more information about transfer learning, you can check out our [Transfer Learning Guide](/en/guides/transfer_learning).
## Transfer Learning in Practice
Here are some common use cases of transfer learning:
- **Image Classification**: Using pre-trained models to classify images into various categories.
- **Object Detection**: Detecting objects in images using pre-trained models.
- **Natural Language Processing**: Using pre-trained models for tasks like sentiment analysis and text classification.
