AI0-001 AI Infrastructure and Technologies Practice Question
A data scientist wants to develop a computer vision model using transfer learning. They need a framework that provides pre-trained models and easy-to-use APIs for data augmentation and training. Which TWO frameworks are best suited for this task?
⚠ Common exam trap
Candidates often select Hugging Face Transformers because it provides pre-trained models, but it is primarily designed for NLP tasks, not computer vision. Similarly, Keras is a high-level API that runs on top of TensorFlow, so it is not considered a standalone framework for this purpose.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
PyTorch
PyTorch (option B) is correct because it offers a rich ecosystem of pre-trained models via `torchvision.models`, along with built-in data augmentation transforms in `torchvision.transforms` and a flexible training loop that is ideal for transfer learning. Its dynamic computation graph makes it easy to modify model architectures for fine-tuning, which is a core requirement for the task.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Hugging Face Transformers
Why it's wrong here
Hugging Face Transformers is primarily for NLP models, not computer vision (though it has some vision models, it's not the best fit for general computer vision transfer learning).
- ✓
PyTorch
Why this is correct
PyTorch provides torchvision with pre-trained models and torchvision.transforms for data augmentation, making it ideal for transfer learning in computer vision.
- ✗
scikit-learn
Why it's wrong here
scikit-learn is for classical machine learning, not deep learning or computer vision transfer learning.
- ✓
TensorFlow
Why this is correct
TensorFlow offers TensorFlow Hub and Keras Applications for pre-trained models, and tf.data for data augmentation, well-suited for computer vision.
- ✗
Keras
Why it's wrong here
Keras is a high-level API that runs on top of TensorFlow; it is not a standalone framework for transfer learning.
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