# schrodingercatss/tuning_playbook_zh_cn

一本系统地教你将深度学习模型的性能最大化的战术手册。

Repository: https://github.com/schrodingercatss/tuning_playbook_zh_cn
Canonical: https://ross.abutalabs.com/products/tuning_playbook_zh_cn
Homepage: https://sourcecode.gitbook.io/ai/
License: NOASSERTION
License Family: other
Last push: 2023-05-27T10:18:45+00:00

## Health v2 (maintenance only)
Score: 31/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 35, longevity 93
- inputs: {"age_days": 1315, "days_push": 1194, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 3221, forks 288 (observed 2026-08-28T04:07:49.769670+00:00)

## What it is
A Chinese translation of the Google Deep Learning Tuning Playbook, a tactical guide by Google Brain researchers on systematically maximizing deep learning model performance. It covers hyperparameter tuning, choosing model architectures and optimizers, batch size selection, and training workflow best practices.

## Use cases
- learn how to tune hyperparameters for deep learning models
- decide which optimizer and batch size to use for a new project
- improve neural network training performance systematically
- find a structured methodology for running ML experiments
- read the deep learning tuning playbook in Chinese
- set up experiment tracking and checkpointing best practices

## When to choose
- you are an ML engineer or researcher wanting practical, systematic guidance on tuning deep learning models
- you prefer reading the Google Brain tuning playbook in Chinese
- you are starting a new deep learning project and need guidance on architecture, optimizer, and batch size choices

## When to avoid
- you need a software tool or library rather than a written guide
- you need coverage of reinforcement learning or non-supervised problem setups in depth
- you require an up-to-date official English version with the latest revisions

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, deep-learning, documentation
- domain: deep-learning, machine-learning, tutorials
- platform: cross-platform
- tags: hyperparameter-tuning, chinese-translation, google-brain, playbook, model-training, neural-networks, localization

## Member repositories
- schrodingercatss/tuning_playbook_zh_cn (main) score 31

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:49.769670+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:44:20.476848+00:00, confidence not recorded.
  - readme: https://github.com/schrodingercatss/tuning_playbook_zh_cn (fetched 2026-08-28T04:07:49.769670+00:00, sha 9467e6fe0504)
  - homepage: https://sourcecode.gitbook.io/ai/ (fetched 2026-08-29T09:38:15.792809+00:00, sha 1350f6734c23)
- Data as of 2026-08-30T08:39:29.467469+00:00.