Recap

Explore core PyTorch training techniques, from creating mini-batch training loops and validation steps to using Dataset and DataLoader classes. Learn essential practices such as model evaluation modes, disabling gradients during validation, logging with TensorBoard, and saving/loading model checkpoints to build effective and manageable models.