Transfer learning is a popular technique in machine learning where a model developed for one task is reused as the starting point for a model on a second task. This tutorial will cover the basics of transfer learning, its benefits, and how it works.
Benefits of Transfer Learning
- Reduced Training Time: Transfer learning can significantly reduce the amount of training data and computational resources required for a new model.
- Improved Performance: Models trained using transfer learning often achieve better performance on new tasks compared to models trained from scratch.
- Domain Adaptation: Transfer learning can help adapt models to new domains with limited data.
How Transfer Learning Works
- Pre-trained Models: Transfer learning typically starts with a pre-trained model that has been trained on a large dataset. These models have learned rich features from the data.
- Feature Extraction: The pre-trained model's layers, especially the deeper layers, are used to extract features from the new dataset.
- Fine-tuning: The extracted features are then used as input to a new model, which is fine-tuned on the new dataset. This helps the model adapt to the specific characteristics of the new task.
Example
Let's say we have a pre-trained model trained on ImageNet, a large visual database designed for use in visual object recognition software research. We can use this model to classify images in a new domain, such as classifying dogs in different breeds.
To do this, we will:
- Load the pre-trained model.
- Extract features from the new images using the model.
- Train a new classifier on the extracted features.
Code Snippet
# Load pre-trained model
model = load_pretrained_model()
# Extract features
features = model.extract_features(new_images)
# Train new classifier
new_classifier = train_classifier(features, new_labels)
Further Reading
For more information on transfer learning, you can visit the following resources:
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