Deep Learning and AI Superhero

4.3 Transfer Learning and Fine-Tuning Pretrained PyTorch Models

In many real-world applications, training a deep learning model from scratch presents significant challenges. These include the scarcity of large, labeled datasets and the substantial computational resources required to train complex models with millions of parameters. Transfer learning offers an elegant solution to these challenges by leveraging knowledge from pre-existing models.

This approach involves taking a model that has been pre-trained on a large, general dataset (such as ImageNet, which contains millions of labeled images across thousands of categories) and adapting it to a new, often more specific task. The key idea is that the features learned by the model on the original ...