Transfer Learning: Reusing Learned Knowledge
Transfer Learning is a method of solving new problems by utilizing pre-trained models.
Instead of training a model from scratch, you take the knowledge from an already trained model and make slight adjustments to fit new data.
This approach allows you to achieve high performance with less data and shorter training time.
Why is Transfer Learning Important?
Training a deep learning model from scratch requires an enormous amount of data and extensive training time.
For example, training an image classification model might require hundreds of thousands of images and could take hours or even days to train.
However, by using transfer learning, you can leverage parts of an existing trained model to gain good performance with much less data and in a fraction of the time.
How Does Transfer Learning Work?
Transfer learning primarily follows these steps:
1. Import a Pre-trained Model
Bring in a large neural network model that has been pre-trained on a massive dataset.
2. Fix Certain Layers
Keep some layers of the pre-trained model as they are and adjust only specific layers to fit the new data.
For instance, in an image classification model, you can reuse existing feature extraction layers (like edge and shape detection) and change only the final output layer to fit the new data.
3. Fine-Tuning with New Data
Conduct additional training with a new dataset to optimize the model.
This process adjusts the model to fit the new problem while maintaining the characteristics learned by the pre-trained model.
Transfer learning allows you to achieve high performance even with limited data and is applicable across various fields such as image classification, natural language processing, and speech recognition.
In the next lesson, we'll apply what we've learned so far and tackle a short quiz.
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