Pretrained Models and Transfer Learning Questions

Working knowledge of pretrained models and transfer learning covers selecting, loading, adapting, and fine tuning models trained on large datasets so they can solve new tasks with limited data. Candidates should understand how to find and use model hubs and repositories such as Hugging Face, PyTorch Hub, and TensorFlow Hub, choose appropriate base models for a problem, and implement both feature extraction approaches where most weights are frozen and full fine tuning where weights are updated. The topic includes parameter efficient adaptation techniques such as adapter modules and low rank adaptation, strategies for freezing or unfreezing layers, data augmentation and domain adaptation considerations, and different optimization and regularization choices when data is scarce. It also spans domain specific practices for computer vision and natural language processing, familiarity with common pretrained sources such as ImageNet trained convolutional networks for vision and transformer based language models for text, evaluation and validation practices, compute and memory trade offs when fine tuning large models, and practical aspects like checkpointing, hyperparameter tuning, and inference deployment.