Transfer Learning with pre-trained models #34
Description
Transfer learning can significantly improve model performance with less training data. Adding examples using pre-trained models (e.g., VGG16, ResNet) for tasks like image classification or feature extraction.
Proposed Examples:
- Include code for fine-tuning the pre-trained model on a new dataset
- Demonstrate the process of feature extraction using pre-trained models
- Provide detailed comments and explanations of each step
Benefits:
- Enhances the repository by including advanced neural network techniques
- Helps users understand and implement transfer learning in their projects
- Shows the performance improvements achievable with transfer learning compared to models trained from scratch
Additional Context:
- Transfer learning is particularly useful when dealing with limited data, as it leverages the knowledge gained from large datasets used to train the pre-trained models.
- Examples can be implemented in Jupyter notebooks to provide an interactive learning experience.
@sanjay-kv I am GSSOC'24 Contributor and would like to work on this issue.