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Deep Learning with PyTorch Step-by-Step: Part I - Fundamentals

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.

We'll cover the following...

General overview

We have covered a lot of ground in this chapter:

  • Writing a higher-order function that builds functions to perform training steps.

  • Understanding PyTorch’s Dataset and TensorDataset classes, implementing its __init__, __get_item__, and __len__ methods.

  • Using PyTorch’s DataLoader class ...