Add a suggestion using the One Cycle Policy with Gradient clipping.

[Shuyib]

I have added a section in the notebook that goes through the One cycle Policy using the PyTorch method with gradient clipping. I have also added a short description of the method which was edited by Claude 3.5 sonnet. It may not be the best but I welcome corrections, and I hope this method will be part of the appendix.

Note: I did not run the code in the cell on this pull request.

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[rasbt]

Thanks for the PR, but I currently can't accept additions to the chapters and the Appendix because they have already been layouted by the publisher and it would be confusing for readers if the code in the notebook would be different than the code in the chapter. Thanks for contributing though!

Btw in the code you mention one-cycle policy, but the code in the appendix already implements this. So, may I ask how it's different and why it's needed?

[Shuyib]

Thank you for your response to the pull request. I greatly appreciate it. The techniques indeed share similarities; however, I believe the core distinction lies in the balance between exploration and exploitation. This is exemplified by the value of the learning rate, which initially increases from a low value to a high value, then subsequently decreases.

[rasbt]

Oh I see, I at first I didn't see the difference because there wasn't a plot and assumed by the title that it was similar but I think I understand the difference now. It's basically a full cycle whereas in the Appendix I have a half-cycle.

To be honest, I do prefer the current half-cycle implementation because that's how it is commonly done in LLM research (e.g., see https://arxiv.org/pdf/2403.08763). I haven't seen any LLM trained with a one-cycle (as opposed to half-cycle) policy, so I'd be a bit hesitant recommending that. Thanks for the PR and the discussion though!