Deep Learning with PyTorch

This chapter covers PyTorch from tensors to production training loops. Each section includes runnable code examples.

• Tensors -- Creating tensors, dtypes, device placement, views vs copies, broadcasting
• Autograd -- Computational graphs, backward(), gradient accumulation, custom autograd functions
• nn.Module -- Module anatomy, parameter registration, saving/loading state
• Data Loading -- Dataset, DataLoader, IterableDataset, collate_fn, streaming
• Training Loop -- Complete training loop with mixed precision, checkpointing, and logging
• Debugging -- Anomaly detection, shape debugging, NaN detection, memory profiling