Exercises

1. Describe the concept of transfer learning.
2. When can the transfer learning process bring good results?
3. What are the differences between transfer learning and fine-tuning?
4. If a model has been trained on a small dataset with low variance (similar examples), is it an excellent candidate to be used as a fixed-feature extractor for transfer learning?
5. The flower classifier built in the transfer learning section has no performance evaluation on the test dataset: add it.
6. Extend the flower classifier source code, making it log the metrics on TensorBoard. Use the summary writers that are already defined.
7. Extend the flower classifier to save the training status using a checkpoint (and its checkpoint manager).
8. Create a second checkpoint for the model that reached the highest validation accuracy.
9. Since the model suffers from overfitting, a good test is to reduce the number of neurons...