Dynamic computation graphs in PyTorch, like torch.autograd.Function, offer flexibility in constructing and executing graphs on-the-fly. This allows for dynamic changes, conditional execution, and recursive functions, aligning closely with how programmers think. Customize your neural networks with dynamic computation graphs for a more intuitive approach.