Data Science and Machine Learning (Theory and Projects) A to Z - Transfer Learning: Practical Tips

The video provides practical tips for transfer learning, emphasizing the importance of following a complete machine learning pipeline, including data splitting for training, validation, and testing. It discusses how the number of trainable layers in a pre-trained model should be adjusted based on the quantity of available data. For small datasets, most layers should remain fixed, while for larger datasets, more layers can be unfrozen. Even when training from scratch, initializing weights with pre-trained values is recommended. The video concludes with a promise of a Python demo in the next session.

What new insight or understanding did you gain from this video?

Evaluate responses using AI:

OFF