Discover key concepts of transfer learning with this quiz focused on fundamental FastAI practices. Assess your ability to identify essential transfer learning steps, benefits, and terminology for beginners interested in efficient model training.
What is the primary purpose of using transfer learning when building a neural network for image classification?
Explanation: Transfer learning uses knowledge from a model trained on a large dataset to improve performance on a related task with less data and time. Increasing training time is not the goal; transfer learning aims for efficiency. Random data generation is unrelated to transfer learning methods. Ignoring pre-trained models and training from scratch defeats the purpose of this technique.
What does it mean to 'freeze' the layers of a neural network in transfer learning?
Explanation: Freezing layers means their weights remain fixed during initial training stages, which helps retain learned features. Deleting layers is not part of this process and would harm model performance. Changing outputs to zero or doubling weights are incorrect interpretations and would not freeze model knowledge effectively.
In transfer learning, what is the main benefit of fine-tuning a model after initially training only the new layers?
Explanation: Fine-tuning helps adjust the pre-trained parts of the model for optimal performance on new data, improving accuracy. Data requirements are often reduced but not eliminated. Changing the architecture automatically is not a part of fine-tuning, and perfect accuracy is never guaranteed just by this step.
Which scenario best demonstrates the practical use of transfer learning?
Explanation: This option describes using a general model for a more specific but related classification, which is ideal for transfer learning. Data collection alone is not transfer learning, and speech recognition uses different data than text classification. Doubling parameters is unrelated to the core idea of transfer learning.
When using transfer learning, which outcome is most commonly observed compared to training a model from scratch on a small dataset?
Explanation: Transfer learning often boosts accuracy and reduces training duration due to prior knowledge embedded in the model. Lower accuracy with longer training contradicts the benefits of this method. Random predictions suggest a broken or improperly trained model, and seeing no performance change would rarely occur if transfer learning is applied properly.
Why is it important to use a pre-trained model whose original task is similar to your new task?
Explanation: If the tasks are similar, the model’s learned features apply better to the new dataset, supporting effective transfer. The dataset size does not change by itself, and slower training is not desired. Validation data remains essential in both transfer learning and traditional training.
What is a standard first step when applying transfer learning to a new classification problem?
Explanation: Transfer learning typically starts by loading a model pre-trained on a similar task and swapping out the final layer for your new classes. Training from scratch does not use transfer learning. Raising the learning rate at the start risks unstable learning, and deleting data unnecessarily is not a recommended practice.
How does using transfer learning help when you have limited labeled training data for your new task?
Explanation: Transfer learning is especially useful with limited labeled data, as the model leverages existing knowledge. There is no requirement that data be unlabeled, though methods for doing so exist. Manually labeling every pixel is only required in specific contexts, not all tasks. Small datasets are not a barrier; they are common in transfer learning applications.
Why might you want to use a lower learning rate when fine-tuning the pre-trained layers of a transfer learning model?
Explanation: A lower learning rate helps preserve important knowledge in pre-trained weights, subtly adapting them to new data. Slowing down training for energy reasons is unrelated. Erasing pre-trained features or resetting learned weights would remove the benefits of transfer learning.
What is the term for the process of adapting a machine learning model trained on one problem to work on a new, but related, problem?
Explanation: Transfer learning describes this process of reusing and adapting a pre-trained model. Regression learning refers to predicting continuous outcomes rather than classification. Transdiction and inference learning are not standard terms in the context of model adaptation.