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Tuning_playbook Alternatives
Similar projects and alternatives to tuning_playbook
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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From-0-to-Research-Scientist-resources-guide
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
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tuning_playbook
A playbook for systematically maximizing the performance of deep learning models. (by fzyzcjy)
NOTE:
The number of mentions on this list indicates mentions on common posts plus user suggested alternatives.
Hence, a higher number means a better tuning_playbook alternative or higher similarity.
tuning_playbook reviews and mentions
Posts with mentions or reviews of tuning_playbook.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-04-14.
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When Random Numbers Are Too Random: Low Discrepancy Sequences
These are also called quasirandom numbers. Despite games, another use case is for hyperparameter search for neural networks.
https://github.com/google-research/tuning_playbook?tab=readm...
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Hyperparameter Optimization for LLMs via Scaling Laws
[2] https://github.com/google-research/tuning_playbook
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Beyond Automatic Differentiation
Batch size can be used for regularisation, but using it for that will limit training performance. From the Google Research Tuning Playbook:
> The batch size governs the training speed and shouldn't be used to directly tune the validation set performance. Often, the ideal batch size will be the largest batch size supported by the available hardware.
> […]
> As long as all hyperparameters are well-tuned (especially the learning rate and regularization hyperparameters) and the number of training steps is sufficient, the same final performance should be attainable using any batch size (see Shallue et al. 2018).
https://github.com/google-research/tuning_playbook#choosing-...
The ideal case is full-batch with tuneable regularisation, just the hardware gets expensive.
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Modeling methodology
Regarding tuning params, this is an excellent read: https://github.com/google-research/tuning_playbook
- About the hardware
- I asked an AI to create an Asmongold story and then had another AI generate voice. There it is dude
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Trending ML repos of the week 📈
3️⃣ google-research/tuning_playbook
- AI全靠偷欧美开源的
- Deep learning tuning playbook
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A note from our sponsor - SaaSHub
www.saashub.com | 3 May 2024
Stats
Basic tuning_playbook repo stats
16
25,053
4.7
12 days ago
google-research/tuning_playbook is an open source project licensed under GNU General Public License v3.0 or later which is an OSI approved license.
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