AILAB Track 3: Deep Learning with PyTorch & Modern CV/NLP

01 / Why a student should open this

Writing clean PyTorch training loops, datasets, dataloaders, and fine-tuning Hugging Face transformers and torchvision vision models.

02 / Student Context & Field Notes

Best for

Computer vision or NLP subteam placement

Time commitment

2-3 weeks

Prerequisites

Python 3, NumPy vector operations, basic neural network concepts.

Best part / timestamp

Clear separation of forward pass, loss calculation, optimizer zero_grad, and backward pass.

Watch out / Caveat

Ensure you use torch.no_grad() during evaluation to avoid GPU out-of-memory errors.

Personal note from contributor

“Keep training loops modular with logging. UFAZ servers have GPUs you can request access to.”

https://pytorch.org/tutorials/