Pytorch 

All course materials are available in the following Google Classroom:
https://classroom.google.com/c/ODMwNDU0OTExODY1?cjc=eswzxamc

Abstract: A practical course aimed at teaching the PyTorch ecosystem: tensors and operations, building custom models, managing datasets and dataloaders, optimization strategies, debugging, saving and loading checkpoints, acceleration techniques (mixed precision), and integration with monitoring tools (TensorBoard, Weights & Biases). Special attention will be dedicated to using the GPUs of the purchased machines: managing multiple devices in PyTorch, memory allocation and best practices for CPU↔GPU data transfer, and using torch.cuda for profiling.