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A playbook for systematically maximizing the performance of deep learning models.

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Just a little cosmetic change (hyper-parameter -> hyperparameter) for consistency

Hello, thank you for the great work. Just curious, do you have any plan for making some multi-language translated version of this great repo? Thanks :)

Hi, Thanks for publishing to everyone for free, I can help you to create an English to Turkish translation. Please let me know If I can help you with this...

Great starting document. I highly recommend that a Delphi process (https://en.wikipedia.org/wiki/Delphi_method) with the larger ML community should be pursued. See example here: https://arxiv.org/pdf/2206.01653.pdf

From a best practices perspective and for better integration with other tools it would be great to see this document convert to a flowchart, if possible.

I can help with creating a Chinese translation of this document. Please let me know if I can help with this issue.

Can we publish this repository on GitHub Pages for ease of accessibility? The process to do so seems quite simple to execute and would be a great addition to this...

Thanks for the nice resource! I believe there is a typo in the [Citing section](https://github.com/google-research/tuning_playbook#citing): Should be `url = {http://github.com/google-research/tuning_playbook}` instead of `url = {http://github.com/google/tuning_playbook}`

Hi thanks for the playbook! I see some articles showing how Hyperband or ASHA can be used to boost the speed of hyperparameter searching. Shortly speaking, it is: > On...