Transfer learning for small datasets in TensorFlow - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is transfer learning in machine learning?

Transfer learning is a method where a model developed for one task is reused as the starting point for a model on a second task. It helps when you have a small dataset by using knowledge from a larger dataset.

beginner

Why is transfer learning useful for small datasets?

Because small datasets often don't have enough data to train a model well from scratch, transfer learning uses a pre-trained model's knowledge to improve learning and reduce training time.

intermediate

In TensorFlow, what is a common step after loading a pre-trained model for transfer learning?

A common step is to freeze the pre-trained layers so their weights don't change, then add new layers on top to train on the small dataset.

beginner

What does 'freezing layers' mean in transfer learning?

Freezing layers means making the weights of some layers unchangeable during training, so the model keeps the learned features from the original task.

intermediate

How can you adapt a pre-trained model to a new classification task with fewer classes?

You replace the final layer(s) of the pre-trained model with new layers that match the number of classes in your new task, then train only those layers or fine-tune some layers.

What is the main advantage of using transfer learning on small datasets?

A It requires more data to train the model.

B It uses knowledge from a larger dataset to improve learning.

C It always trains the entire model from scratch.

D It ignores pre-trained weights.

Transfer learning leverages knowledge from a model trained on a large dataset to help train on a smaller dataset.

In TensorFlow, what does freezing layers do during transfer learning?

A Prevents the weights of those layers from updating during training.

B Deletes those layers from the model.

C Adds new layers to the model.

D Increases the learning rate for those layers.

Freezing layers means their weights stay fixed and do not update during training.

Which step is usually done first when applying transfer learning with TensorFlow?

A Randomly initialize weights.

B Train a model from scratch.

C Load a pre-trained model.

D Delete all layers.

You start by loading a pre-trained model to reuse its learned features.

How do you handle a different number of classes in your new task compared to the pre-trained model?

A Use the pre-trained model without any changes.

B Keep the original final layer unchanged.

C Remove all layers except the first.

D Replace the final layer with one matching the new number of classes.

The final layer must be replaced to output predictions for the new classes.

What is a common practice to avoid overfitting when training on small datasets with transfer learning?

A Freeze most pre-trained layers and train only new layers.

B Train all layers with a high learning rate.

C Use no pre-trained model.

D Increase the dataset size by copying data.

Freezing most layers helps keep learned features and reduces overfitting on small data.

Explain how transfer learning helps when you have a small dataset. Include the role of freezing layers and adapting the final layer.

Think about reusing knowledge and adjusting the model for your task.

Describe the steps to apply transfer learning in TensorFlow for a new image classification task with limited data.

Focus on loading, freezing, replacing, compiling, and training.