Gradient checkpointing

Gradient checkpointing: A technique storing selected activations and recomputing others during backpropagation to reduce training memory. It trades extra computation for lower memory use. Checkpoint placement determines how much forward work must be repeated. This perspective connects the definition with practical behaviour in artificial intelligence systems.