Episode 9 - PyTorch Module state скачать в хорошем качестве

Episode 9 - PyTorch Module state

*Episode 9 – “PyTorch Module State”* In this episode we finally uncover the secret sauce that lets you save, transfer, and resume training: **the module state**. We’ll walk through every step of how a `torch.nn.Module` turns its parameters and buffers into a clean, serialisable dictionary with `state_dict()`, and how that same dictionary can be used to restore the exact same model with `load_state_dict()`. --- What you’ll learn *What lives inside a `state_dict`* – a quick tour of parameters, buffers, and registered buffers *Why `state_dict()` is deterministic* – how the order of the keys is preserved *State correctness* – the `strict` flag. *Customising state loading* – using hooks to transform weights on the fly. --- Why watch? If you’ve ever wondered how to *robustly checkpoint* a network, *transfer‑learn* a head, or *share* a pre‑trained model with colleagues, this video gives you the why and how behind `state_dict()`. By the end you’ll be able to load and save any module confidently, knowing exactly what is being stored and how the integrity of your model is maintained. 📌 *Don’t forget* to subscribe, hit the bell, and drop a comment with any questions or topics you’d like us to cover next! Happy coding!