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Raw Queries and Responses

1. Shard Calculation

Query:

Response:

Calculated Shard: 174 (from laksa065)

2. Crawled Status Check

Query:

curl -X POST \
  'http://laksa174.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://github.com/INTERMT/Awesome-TensorFlow-Chinese\')), getAhrefsUnparsedNoserviceFromURL(\'https://github.com/INTERMT/Awesome-TensorFlow-Chinese\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese","crawl_time":1756366230,"first_indexed_time":0,"http_code":200,"src_unparsed":"com,github!\/INTERMT\/Awesome-TensorFlow-Chinese s443","src_root_hash":"6325672905007345774","history_drop_reason":null,"meta_title":"GitHub - INTERMT\/Awesome-TensorFlow-Chinese: 【干货】史上最全的Tensorflow学习资源汇总","meta_descriptions":["【干货】史上最全的Tensorflow学习资源汇总. Contribute to INTERMT\/Awesome-TensorFlow-Chinese development by creating an account on GitHub."],"attrs_boilerpipe_text":"\n    You can’t perform that action at this time.\n  ","attrs_markdown":"[Skip to content](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese#start-of-content)\n\n## Navigation Menu\nToggle navigation\n\n[Sign in](https:\/\/github.com\/login?return_to=https%3A%2F%2Fgithub.com%2FINTERMT%2FAwesome-TensorFlow-Chinese)\n\nAppearance settings\n\n- Platform\n  - [GitHub Copilot Write better code with AI](https:\/\/github.com\/features\/copilot)\n  - [GitHub Spark New Build and deploy intelligent apps](https:\/\/github.com\/features\/spark)\n  - [GitHub Models New Manage and compare prompts](https:\/\/github.com\/features\/models)\n  - [GitHub Advanced Security Find and fix vulnerabilities](https:\/\/github.com\/security\/advanced-security)\n  - [Actions Automate any workflow](https:\/\/github.com\/features\/actions)\n  \n  - [Codespaces Instant dev environments](https:\/\/github.com\/features\/codespaces)\n  - 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[Reload](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese) to refresh your session.\n\nDismiss alert\n\n{{ message }}\n\n[INTERMT](https:\/\/github.com\/INTERMT)   \/  **[Awesome-TensorFlow-Chinese](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese)**  Public\n\n- [Notifications](https:\/\/github.com\/login?return_to=%2FINTERMT%2FAwesome-TensorFlow-Chinese)\n  You must be signed in to change notification settings\n- [Fork 16](https:\/\/github.com\/login?return_to=%2FINTERMT%2FAwesome-TensorFlow-Chinese)\n- [Star 60](https:\/\/github.com\/login?return_to=%2FINTERMT%2FAwesome-TensorFlow-Chinese)\n\n【干货】史上最全的Tensorflow学习资源汇总\n\n### License\n[Apache-2.0 license](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/blob\/master\/LICENSE)\n\n[60 stars](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/stargazers)\n\n[16 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[Insights](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/pulse)\n\n# INTERMT\/Awesome-TensorFlow-Chinese\nmaster\n\n[Branches](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/branches)\n\n[Tags](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/tags)\n\nGo to file\n\nCode\n\nOpen more actions menu\n\n## Folders and files\n| Name | Name | Last commit message | Last commit date |\n|---|---|---|---|\n| Latest commit History [4 Commits](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/commits\/master\/) |   |   |   |\n| [LICENSE](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/blob\/master\/LICENSE \"LICENSE\") | [LICENSE](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/blob\/master\/LICENSE \"LICENSE\") |   |   |\n| [README.md](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/blob\/master\/README.md \"README.md\") | [README.md](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/blob\/master\/README.md \"README.md\") |   |   |\n| View all files |   |   |   |\n\n## Repository files navigation\n- [README](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese)\n- [Apache-2.0 license](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese)\n\n## 目录\n- [Tensorflow教程资源](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese#Tensorflow%E6%95%99%E7%A8%8B%E8%B5%84%E6%BA%90)\n- [Tensorflow视频资源](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese#Tensorflow%E8%A7%86%E9%A2%91%E8%B5%84%E6%BA%90)\n- [Tensorflow项目资源](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese#Tensorflow%E9%A1%B9%E7%9B%AE%E8%B5%84%E6%BA%90)\n- [适合新手学习的Tensorflow书籍](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese#%E9%80%82%E5%90%88%E6%96%B0%E6%89%8B%E5%AD%A6%E4%B9%A0%E7%9A%84Tensorflow%E4%B9%A6%E7%B1%8D)\n## Tensorflow教程资源\n- [适合初学者的Tensorflow教程和代码示例](https:\/\/github.com\/aymericdamien\/TensorFlow-Examples)：该教程不光提供了一些经典的数据集，更是从实现最简单的“Hello World”开始，到机器学习的经典算法，再到神经网络的常用模型，一步步带你从入门到精通，是初学者学习Tensorflow的最佳教程。\n- [从Tensorflow基础知识到有趣的项目应用](https:\/\/github.com\/pkmital\/tensorflow_tutorials)：同样是适合新手的教程，从安装到项目实战，教你搭建一个属于自己的神经网络。\n- [使用Jupyter Notebook运行的TensorFlow教程](https:\/\/github.com\/sjchoi86\/Tensorflow-101)：本教程是基于Jupyter Notebook开发环境的Tensorflow教程，Jupyter Notebook是一款非常好用的交互式开发工具，不仅支持40多种编程语言，还可以实时运行代码、共享文档、数据可视化、支持markdown等，适用于机器学习、统计建模数据处理、特征提取等多个领域。\n- [构建您的第一款TensorFlow Android应用程序](https:\/\/omid.al\/posts\/2017-02-20-Tutorial-Build-Your-First-Tensorflow-Android-App.html)：本教程可帮助您从零开始将张量流模型引入到Android应用程序。\n- [Tensorflow代码练习](https:\/\/github.com\/terryum\/TensorFlow_Exercises)：一个从易到难的Tensorflow代码练习手册。非常适合学习Tensorflow的小伙伴。\n## Tensorflow视频资源\n- [TF Girls 修炼指南](https:\/\/www.youtube.com\/watchv=TrWqRMJZU8A&list=PLwY2GJhAPWRcZxxVFpNhhfivuW0kX15yG&index=2)：一个Tensorflow从零开始的公开视频课程，课程偏基础、入门，但知识点讲的非常详细。\n- [炼数成金Tensorflow公开课](https:\/\/www.youtube.com\/watchv=eAtGqz8ytOI&list=PLjSwXXbVlK6IHzhLOMpwHHLjYmINRstrk)：非常不错的课程，推荐给大家。\n- [台湾国立大学李宏毅深度学习的课程](https:\/\/link.zhihu.com\/?target=https%3A\/\/www.bilibili.com\/video\/av9770302\/)：非常值得推荐给大家。\n- 英文不错的小伙伴，也为大家推荐一些国外大牛的英文课程：[链接1](https:\/\/www.youtube.com\/watch?v=vq2nnJ4g6N0)；[链接2](http:\/\/bit.ly\/1OX8s8Y)；[链接3](https:\/\/www.youtube.com\/watch?v=GZBIPwdGtkk&feature=youtu.be&list=PLBkISg6QfSX9HL6us70IBs9slFciFFa4W)\n- [斯坦福大学Tensorflow系列的课程](https:\/\/www.youtube.com\/watch?v=g-EvyKpZjmQ&index=1&list=PLIDllPt3EQZoS8gCP3cw273Cq9puuPLTg)； [课程主页](http:\/\/web.stanford.edu\/class\/cs20si\/index.html)；[课程所有的ppt和笔记notes](https:\/\/pan.baidu.com\/s\/1o8uOQpW)； [github地址](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/blob\/master\/chiphuyen\/tf-stanford-tutorials)：\n- [Tensorflow官网视频教程](https:\/\/developers.google.cn\/machine-learning\/crash-course\/)：针对Tensorflow初级学习的小伙伴还是非常不错的一套课程，有助于大家快速入门。\n## Tensorflow项目资源\n- [一个实现实现Alex Graves论文的随机手写生成的案例](https:\/\/github.com\/hardmaru\/write-rnn-tensorflow)。\n- [基于Tensorflow的生成对抗文本到图像合成](https:\/\/github.com\/zsdonghao\/text-to-image)：如下图所示，该项目是基于Tensorflow的DCGAN模型，教大家一步步从对抗生成文本到图像合成。\n- [基于注意力的图像字幕生成器](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/blob\/master)(<https:\/\/github.com\/yunjey\/show-attend-and-tell)%EF%BC%9A%E8%AF%A5%E6%A8%A1%E5%9E%8B%E5%BC%95%E5%85%A5%E4%BA%86%E5%9F%BA%E4%BA%8E%E6%B3%A8%E6%84%8F%E5%8A%9B%E7%9A%84%E5%9B%BE%E5%83%8F%E6%A0%87%E9%A2%98%E7%94%9F%E6%88%90%E5%99%A8%E3%80%82%E5%8F%AF%E4%BB%A5%E5%B0%86%E5%85%B6%E6%B3%A8%E6%84%8F%E5%8A%9B%E8%BD%AC%E7%A7%BB%E5%88%B0%E5%9B%BE%E5%83%8F%E7%9A%84%E7%9B%B8%E5%85%B3%E9%83%A8%E5%88%86%EF%BC%8C%E5%90%8C%E6%97%B6%E7%94%9F%E6%88%90%E6%AF%8F%E4%B8%AA%E5%8D%95%E8%AF%8D%E3%80%82>\n- [神经网络着色灰度图像](https:\/\/github.com\/pavelgonchar\/colornet)：一个非常有趣且应用场景非常广的一个项目，使用神经网络着色灰度图像。\n- [基于Facebook中FastText的简单嵌入式文本分类器](https:\/\/github.com\/apcode\/tensorflow_fasttext)：该项目是源于Facebook中的FastText的想法，并在Tensorflow中实施。FastText是一款快速的文本分类器，提供简单而高效的文本分类和表征学习的方法。\n- [用Tensorflow实现“基于句子分类的卷积神经网络”](https:\/\/github.com\/dennybritz\/cnn-text-classification-tf)\n- [使用OpenStreetMap功能和卫星图像训练TensorFlow神经网络](https:\/\/github.com\/jtoy\/awesome-tensorflow)：该项目是通过使用OpenStreetMap（OSM）数据训练神经网络，进而对卫星图像中的特征进行分类。\n- [用Tensorflow实现YOLO](https:\/\/github.com\/thtrieu\/darkflow)：“实时对象检测”，并支持实时在移动设备上运行的一个小项目，计算机视觉领域研究者的最佳福利。\n## 适合新手学习的Tensorflow书籍\n- ***《Tensorflow:实战Google深度学习框架》*** ：这本由电子工业出版社出版的Google Tensorflow实战书籍是最早的Tensorflow书籍之一。虽然内容不是特别的系统，CNN、RNN部分介绍的不够具体以及并没有涉及到深度强化学习的内容，但书中对一些基础知识讲解的通俗易懂，另外还增加了可视化工具TensorBoard和分布式加速的章节，为这本书的整体评分增色不少。可见作者还是比较用心的，站能够在初学者的角度为大家讲解深度学习和Tensorflow的知识。\n- ***《Tensorflow机器学习实战指南》***：本书是由资深数据科学家Nick McClure完成的一本Tensorflow实战类书籍。本书的特色是每一小节都讲一小部分原理，让后动手实现相应的代码。虽然原理部分讲的不是很详细，但代码部分讲得细致入微，从机器学习到深度学习的算法，作者都把每部分代码讲的很透彻。对于喜欢手撸代码的小伙伴，这本书还是特别值得推荐的。\n- ***《白话深度学习与TensorFlow》*** ：最后再给大家推荐一本《白话深度学习与TensorFlow》，之前看过作者出的《白话大数据与机器学习》，很喜欢作者的写作风格。书中把很多数学公式、深度学习的原理部分讲成了大白话，很适合小白学习的一本书。但正是因为作者的写作风格，书籍中有很多地方写的不是很严谨；此外在代码方面写的不够详细，整个篇幅的粘贴和复制，代码部分对读者不是很友好。\n\n## About\n【干货】史上最全的Tensorflow学习资源汇总\n\n### Resources\n[Readme](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese#readme-ov-file)\n\n### License\n[Apache-2.0 license](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese#Apache-2.0-1-ov-file)\n\n### Uh oh\\!\nThere was an error while loading. [Please reload this page](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese).\n\n[Activity](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/activity)\n\n### Stars\n[**60** stars](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/stargazers)\n\n### Watchers\n[**4** watching](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/watchers)\n\n### Forks\n[**16** forks](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/forks)\n\n[Report repository](https:\/\/github.com\/contact\/report-content?content_url=https%3A%2F%2Fgithub.com%2FINTERMT%2FAwesome-TensorFlow-Chinese&report=INTERMT+%28user%29)\n\n## [Releases](https:\/\/github.com\/INTERMT\/Awesome-TensorFlow-Chinese\/releases)\nNo releases published\n\n## [Packages 0](https:\/\/github.com\/users\/INTERMT\/packages?repo_name=Awesome-TensorFlow-Chinese)\nNo packages published\n\n## Footer\n© 2025 GitHub, Inc.\n\n### Footer navigation\n- [Terms](https:\/\/docs.github.com\/site-policy\/github-terms\/github-terms-of-service)\n- [Privacy](https:\/\/docs.github.com\/site-policy\/privacy-policies\/github-privacy-statement)\n- [Security](https:\/\/github.com\/security)\n- [Status](https:\/\/www.githubstatus.com\/)\n- [Docs](https:\/\/docs.github.com\/)\n- [Contact](https:\/\/support.github.com\/?tags=dotcom-footer)\n- Manage cookies\n- Do not share my personal information\n\nYou can’t perform that action at this time.","attrs_readable_markdown":null,"meta_canonical":null}

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Page Info Filters

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	PASS	`download_stamp > now() - 6 MONTH`	7.6 months ago (distributed domain, exempt)
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

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Property	Value
URL	https://github.com/INTERMT/Awesome-TensorFlow-Chinese
Last Crawled	2025-08-28 07:30:30 (7 months ago)
First Indexed	not set
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[README](https://github.com/INTERMT/Awesome-TensorFlow-Chinese) - [Apache-2.0 license](https://github.com/INTERMT/Awesome-TensorFlow-Chinese) ## 目录 - [Tensorflow教程资源](https://github.com/INTERMT/Awesome-TensorFlow-Chinese#Tensorflow%E6%95%99%E7%A8%8B%E8%B5%84%E6%BA%90) - [Tensorflow视频资源](https://github.com/INTERMT/Awesome-TensorFlow-Chinese#Tensorflow%E8%A7%86%E9%A2%91%E8%B5%84%E6%BA%90) - [Tensorflow项目资源](https://github.com/INTERMT/Awesome-TensorFlow-Chinese#Tensorflow%E9%A1%B9%E7%9B%AE%E8%B5%84%E6%BA%90) - [适合新手学习的Tensorflow书籍](https://github.com/INTERMT/Awesome-TensorFlow-Chinese#%E9%80%82%E5%90%88%E6%96%B0%E6%89%8B%E5%AD%A6%E4%B9%A0%E7%9A%84Tensorflow%E4%B9%A6%E7%B1%8D) ## Tensorflow教程资源 - [适合初学者的Tensorflow教程和代码示例](https://github.com/aymericdamien/TensorFlow-Examples)：该教程不光提供了一些经典的数据集，更是从实现最简单的“Hello World”开始，到机器学习的经典算法，再到神经网络的常用模型，一步步带你从入门到精通，是初学者学习Tensorflow的最佳教程。 - [从Tensorflow基础知识到有趣的项目应用](https://github.com/pkmital/tensorflow_tutorials)：同样是适合新手的教程，从安装到项目实战，教你搭建一个属于自己的神经网络。 - [使用Jupyter Notebook运行的TensorFlow教程](https://github.com/sjchoi86/Tensorflow-101)：本教程是基于Jupyter Notebook开发环境的Tensorflow教程，Jupyter Notebook是一款非常好用的交互式开发工具，不仅支持40多种编程语言，还可以实时运行代码、共享文档、数据可视化、支持markdown等，适用于机器学习、统计建模数据处理、特征提取等多个领域。 - [构建您的第一款TensorFlow Android应用程序](https://omid.al/posts/2017-02-20-Tutorial-Build-Your-First-Tensorflow-Android-App.html)：本教程可帮助您从零开始将张量流模型引入到Android应用程序。 - [Tensorflow代码练习](https://github.com/terryum/TensorFlow_Exercises)：一个从易到难的Tensorflow代码练习手册。非常适合学习Tensorflow的小伙伴。 ## Tensorflow视频资源 - [TF Girls 修炼指南](https://www.youtube.com/watchv=TrWqRMJZU8A&list=PLwY2GJhAPWRcZxxVFpNhhfivuW0kX15yG&index=2)：一个Tensorflow从零开始的公开视频课程，课程偏基础、入门，但知识点讲的非常详细。 - [炼数成金Tensorflow公开课](https://www.youtube.com/watchv=eAtGqz8ytOI&list=PLjSwXXbVlK6IHzhLOMpwHHLjYmINRstrk)：非常不错的课程，推荐给大家。 - [台湾国立大学李宏毅深度学习的课程](https://link.zhihu.com/?target=https%3A//www.bilibili.com/video/av9770302/)：非常值得推荐给大家。 - 英文不错的小伙伴，也为大家推荐一些国外大牛的英文课程：[链接1](https://www.youtube.com/watch?v=vq2nnJ4g6N0)；[链接2](http://bit.ly/1OX8s8Y)；[链接3](https://www.youtube.com/watch?v=GZBIPwdGtkk&feature=youtu.be&list=PLBkISg6QfSX9HL6us70IBs9slFciFFa4W) - [斯坦福大学Tensorflow系列的课程](https://www.youtube.com/watch?v=g-EvyKpZjmQ&index=1&list=PLIDllPt3EQZoS8gCP3cw273Cq9puuPLTg)； [课程主页](http://web.stanford.edu/class/cs20si/index.html)；[课程所有的ppt和笔记notes](https://pan.baidu.com/s/1o8uOQpW)； [github地址](https://github.com/INTERMT/Awesome-TensorFlow-Chinese/blob/master/chiphuyen/tf-stanford-tutorials)： - [Tensorflow官网视频教程](https://developers.google.cn/machine-learning/crash-course/)：针对Tensorflow初级学习的小伙伴还是非常不错的一套课程，有助于大家快速入门。 ## Tensorflow项目资源 - [一个实现实现Alex Graves论文的随机手写生成的案例](https://github.com/hardmaru/write-rnn-tensorflow)。 - [基于Tensorflow的生成对抗文本到图像合成](https://github.com/zsdonghao/text-to-image)：如下图所示，该项目是基于Tensorflow的DCGAN模型，教大家一步步从对抗生成文本到图像合成。 - [基于注意力的图像字幕生成器](https://github.com/INTERMT/Awesome-TensorFlow-Chinese/blob/master)(<https://github.com/yunjey/show-attend-and-tell)%EF%BC%9A%E8%AF%A5%E6%A8%A1%E5%9E%8B%E5%BC%95%E5%85%A5%E4%BA%86%E5%9F%BA%E4%BA%8E%E6%B3%A8%E6%84%8F%E5%8A%9B%E7%9A%84%E5%9B%BE%E5%83%8F%E6%A0%87%E9%A2%98%E7%94%9F%E6%88%90%E5%99%A8%E3%80%82%E5%8F%AF%E4%BB%A5%E5%B0%86%E5%85%B6%E6%B3%A8%E6%84%8F%E5%8A%9B%E8%BD%AC%E7%A7%BB%E5%88%B0%E5%9B%BE%E5%83%8F%E7%9A%84%E7%9B%B8%E5%85%B3%E9%83%A8%E5%88%86%EF%BC%8C%E5%90%8C%E6%97%B6%E7%94%9F%E6%88%90%E6%AF%8F%E4%B8%AA%E5%8D%95%E8%AF%8D%E3%80%82> - [神经网络着色灰度图像](https://github.com/pavelgonchar/colornet)：一个非常有趣且应用场景非常广的一个项目，使用神经网络着色灰度图像。 - [基于Facebook中FastText的简单嵌入式文本分类器](https://github.com/apcode/tensorflow_fasttext)：该项目是源于Facebook中的FastText的想法，并在Tensorflow中实施。FastText是一款快速的文本分类器，提供简单而高效的文本分类和表征学习的方法。 - [用Tensorflow实现“基于句子分类的卷积神经网络”](https://github.com/dennybritz/cnn-text-classification-tf) - [使用OpenStreetMap功能和卫星图像训练TensorFlow神经网络](https://github.com/jtoy/awesome-tensorflow)：该项目是通过使用OpenStreetMap（OSM）数据训练神经网络，进而对卫星图像中的特征进行分类。 - [用Tensorflow实现YOLO](https://github.com/thtrieu/darkflow)：“实时对象检测”，并支持实时在移动设备上运行的一个小项目，计算机视觉领域研究者的最佳福利。 ## 适合新手学习的Tensorflow书籍 - *《Tensorflow:实战Google深度学习框架》* ：这本由电子工业出版社出版的Google Tensorflow实战书籍是最早的Tensorflow书籍之一。虽然内容不是特别的系统，CNN、RNN部分介绍的不够具体以及并没有涉及到深度强化学习的内容，但书中对一些基础知识讲解的通俗易懂，另外还增加了可视化工具TensorBoard和分布式加速的章节，为这本书的整体评分增色不少。可见作者还是比较用心的，站能够在初学者的角度为大家讲解深度学习和Tensorflow的知识。 - *《Tensorflow机器学习实战指南》：本书是由资深数据科学家Nick McClure完成的一本Tensorflow实战类书籍。本书的特色是每一小节都讲一小部分原理，让后动手实现相应的代码。虽然原理部分讲的不是很详细，但代码部分讲得细致入微，从机器学习到深度学习的算法，作者都把每部分代码讲的很透彻。对于喜欢手撸代码的小伙伴，这本书还是特别值得推荐的。 - 《白话深度学习与TensorFlow》* ：最后再给大家推荐一本《白话深度学习与TensorFlow》，之前看过作者出的《白话大数据与机器学习》，很喜欢作者的写作风格。书中把很多数学公式、深度学习的原理部分讲成了大白话，很适合小白学习的一本书。但正是因为作者的写作风格，书籍中有很多地方写的不是很严谨；此外在代码方面写的不够详细，整个篇幅的粘贴和复制，代码部分对读者不是很友好。 ## About 【干货】史上最全的Tensorflow学习资源汇总 ### Resources [Readme](https://github.com/INTERMT/Awesome-TensorFlow-Chinese#readme-ov-file) ### License [Apache-2.0 license](https://github.com/INTERMT/Awesome-TensorFlow-Chinese#Apache-2.0-1-ov-file) ### Uh oh\! 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