{"id":20322370,"url":"https://github.com/retval/aibook","last_synced_at":"2025-03-04T10:13:32.999Z","repository":{"id":69167148,"uuid":"156373111","full_name":"RetVal/aiBook","owner":"RetVal","description":"AiBook","archived":false,"fork":false,"pushed_at":"2018-07-19T03:16:26.000Z","size":66945,"stargazers_count":0,"open_issues_count":0,"forks_count":2,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-01-14T13:56:04.358Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":false,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/RetVal.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-11-06T11:27:16.000Z","updated_at":"2022-01-05T14:23:34.000Z","dependencies_parsed_at":"2023-09-15T05:15:57.383Z","dependency_job_id":null,"html_url":"https://github.com/RetVal/aiBook","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RetVal%2FaiBook","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RetVal%2FaiBook/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RetVal%2FaiBook/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RetVal%2FaiBook/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RetVal","download_url":"https://codeload.github.com/RetVal/aiBook/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":241827165,"owners_count":20026601,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-14T19:21:13.914Z","updated_at":"2025-03-04T10:13:32.977Z","avatar_url":"https://github.com/RetVal.png","language":null,"funding_links":[],"categories":[],"sub_categories":[],"readme":"## 1. TensorFlow源码\n* [https://github.com/tensorflow/tensorflow](https://github.com/tensorflow/tensorflow)\n\n## 2. 基于TensorFlow的框架\n* [https://github.com/fchollet/keras](https://github.com/fchollet/keras) \n* [https://github.com/tflearn/tflearn](https://github.com/tflearn/tflearn) \n* [https://github.com/beniz/deepdetect](https://github.com/beniz/deepdetect) \n* [https://github.com/tensorflow/fold](https://github.com/tensorflow/fold) \n* [https://github.com/leriomaggio/deep-learning-keras-tensorflow】(https://github.com/leriomaggio/deep-learning-keras-tensorflow)\n\n## 3. 项目/模型\n* [https://github.com/tensorflow/models](https://github.com/tensorflow/models) \n* [https://github.com/aymericdamien/TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples) \n* [https://github.com/donnemartin/data-science-ipython-notebooks](https://github.com/donnemartin/data-science-ipython-notebooks) \n* [https://github.com/jtoy/awesome-tensorflow](https://github.com/jtoy/awesome-tensorflow) \n* [https://github.com/jikexueyuanwiki/tensorflow-zh](https://github.com/jikexueyuanwiki/tensorflow-zh) \n* [https://github.com/nlintz/TensorFlow-Tutorials](https://github.com/nlintz/TensorFlow-Tutorials) \n* [https://github.com/pkmital/tensorflow_tutorials](https://github.com/pkmital/tensorflow_tutorials) \n* [https://github.com/deepmind/learning-to-learn](https://github.com/deepmind/learning-to-learn) \n* [https://github.com/BinRoot/TensorFlow-Book](https://github.com/BinRoot/TensorFlow-Book) \n* [https://github.com/jostmey/NakedTensor](https://github.com/jostmey/NakedTensor) \n* [https://github.com/alrojo/tensorflow-tutorial](https://github.com/alrojo/tensorflow-tutorial) \n* [https://github.com/CreatCodeBuild/TensorFlow-and-DeepLearning-Tutorial](https://github.com/CreatCodeBuild/TensorFlow-and-DeepLearning-Tutorial) \n* [https://github.com/sjchoi86/Tensorflow-101](https://github.com/sjchoi86/Tensorflow-101) \n* [https://github.com/chiphuyen/tf-stanford-tutorials](https://github.com/chiphuyen/tf-stanford-tutorials) \n* [https://github.com/google/prettytensor](https://github.com/google/prettytensor) \n* [https://github.com/ahangchen/GDLnotes](https://github.com/ahangchen/GDLnotes) \n* [https://github.com/Hvass-Labs/TensorFlow-Tutorials](https://github.com/Hvass-Labs/TensorFlow-Tutorials) \n* [https://github.com/NickShahML/tensorflow_with_latest_papers](https://github.com/NickShahML/tensorflow_with_latest_papers) \n* [https://github.com/nfmcclure/tensorflow_cookbook](https://github.com/nfmcclure/tensorflow_cookbook) \n* [https://github.com/ppwwyyxx/tensorpack](https://github.com/ppwwyyxx/tensorpack) \n* [https://github.com/rasbt/deep-learning-book](https://github.com/rasbt/deep-learning-book) \n* [https://github.com/pkmital/CADL](https://github.com/pkmital/CADL) \n* [https://github.com/tensorflow/skflow](https://github.com/tensorflow/skflow)\n*   [TensorFlow Tutorial 1](https://github.com/pkmital/tensorflow_tutorials) — 从基础到更有趣的 TensorFlow 应用\n*   [TensorFlow Tutorial 2](https://github.com/nlintz/TensorFlow-Tutorials) — 基于 Google TensorFlow 框架的深度学习简介，这些教程是 Newmu 的Theano 直接端口\n*   [TensorFlow Examples](https://github.com/aymericdamien/TensorFlow-Examples) — 给初学者的 TensorFlow 教程和代码示例\n*   [Sungjoon's TensorFlow-101](https://github.com/sjchoi86/Tensorflow-101) — 通过 Python 使用 Jupyter Notebook 编写的 TensorFlow 教程\n*   [Terry Um’s TensorFlow Exercises](https://github.com/terryum/TensorFlow_Exercises) — 从其他 TensorFlow 示例重新创建代码\n*   [Installing TensorFlow on Raspberry Pi 3](https://github.com/samjabrahams/tensorflow-on-raspberry-pi) — TensorFlow 在树莓派上正确编译和运行\n*   [Classification on time series](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition) — 在 TensorFlow 中使用 LSTM 对手机传感器数据进行递归神经网络分类\n*   [Show, Attend and Tell](https://github.com/yunjey/show_attend_and_tell)  — 基于聚焦机制的图像字幕生成器（聚焦机制「Attention Mechanism」是当下深度学习前沿热点之一，能够逐个关注输入的不同部分，给出一系列理解）\n*   [Neural Style](https://github.com/cysmith/neural-style-tf) — Neural Style 的实现（Neural Style 是让机器模仿已有画作的绘画风格把一张图片重新绘制的算法）\n*   [Pretty Tensor](https://github.com/google/prettytensor) — Pretty Tensor 提供了一个高级构建器 API\n*   [Neural Style](https://github.com/anishathalye/neural-style) — Neural Style 的实现\n*   [TensorFlow White Paper Notes](https://github.com/samjabrahams/tensorflow-white-paper-notes) — 带注释的笔记和 TensorFlow 白皮书的摘要，以及 SVG 图形和文档链接\n*   [NeuralArt](https://github.com/ckmarkoh/neuralart_tensorflow) — 艺术风格神经算法的实现\n*   [使用 TensorFlow 和 PyGame 来深度强化学习乒乓球](http://www.danielslater.net/2016/03/deep-q-learning-pong-with-tensorflow.html)\n*   [Generative Handwriting Demo using TensorFlow](https://github.com/hardmaru/write-rnn-tensorflow) — 尝试实现 Alex Graves 的论文中随机手写生成部分\n*   [Neural Turing Machine in TensorFlow](https://github.com/carpedm20/NTM-tensorflow) — 神经图灵机的 TensorFlow 实现\n*   [GoogleNet Convolutional Neural Network Groups Movie Scenes By Setting](https://github.com/agermanidis/thingscoop) — 根据对象，地点和其中显示的其他内容来搜索、过滤和描述视频\n*   [Neural machine translation between the writings of Shakespeare and modern English using TensorFlow](https://github.com/tokestermw/tensorflow-shakespeare) — 单语翻译，从现代英语到莎士比亚，反之亦然\n*   [Chatbot](https://github.com/Conchylicultor/DeepQA)  — “一个神经会话模型”的实现\n*   [Colornet - Neural Network to colorize grayscale images](https://github.com/pavelgonchar/colornet) — 通过神经网络给灰度图像着色\n*   [Neural Caption Generator with Attention](https://github.com/jazzsaxmafia/show_attend_and_tell.tensorflow) — 图像理解的 Tensorflow 实现\n*   [Weakly_detector](https://github.com/jazzsaxmafia/Weakly_detector) — “学习深层特征以区分本地化”的 TensorFlow 实现\n*   [Dynamic Capacity Networks](https://github.com/jazzsaxmafia/dcn.tf) — “动态容量网络”的实现\n*   [HMM in TensorFlow](https://github.com/dwiel/tensorflow_hmm) — HMM 的维特比和前向/后向算法的实现\n*   [DeepOSM](https://github.com/trailbehind/DeepOSM)  — 使用 OpenStreetMap 功能和卫星图像训练 TensorFlow 神经网络\n*   [DQN-tensorflow](https://github.com/devsisters/DQN-tensorflow) — 使用 TensorFlow 通过 OpenAI Gym 实现 DeepMind 的“通过深度强化学习的人类水平控制”\n*   [Highway Network](https://github.com/fomorians/highway-cnn) — [\"深度网络训练\"](http://arxiv.org/abs/1507.06228) 的 TensorFlow 实现\n*   [Sentence Classification with CNN](https://github.com/dennybritz/cnn-text-classification-tf) — TensorFlow 实现“卷积神经网络的句子分类”\n*   [End-To-End Memory Networks](https://github.com/domluna/memn2n) — 端到端记忆网络的实现\n*   [Character-Aware Neural Language Models](https://github.com/carpedm20/lstm-char-cnn-tensorflow)  — 字符感知神经语言模型的 TensorFlow 实现\n*   [YOLO TensorFlow ++](https://github.com/thtrieu/yolotf) — TensorFlow 实现的 “YOLO：实时对象检测”，具有训练和支持在移动设备上实时运行的功能\n*   [Wavenet](https://github.com/ibab/tensorflow-wavenet) — WaveNet 生成神经网络架构的 TensorFlow 实现，用于生成音频\n*   [Mnemonic Descent Method](https://github.com/trigeorgis/mdm) — 助记符下降法：应用于端对端对准的复现过程\n* [无人驾驶](https://github.com/kevinhughes27/TensorKart) \n* [无人驾驶](https://github.com/SullyChen/Autopilot-TensorFlow)\n* [深度强化学习](https://github.com/dennybritz/reinforcement-learning) \n* [深度强化学习](https://github.com/zsdonghao/tensorlayer) \n* [深度强化学习](https://github.com/matthiasplappert/keras-rl) \n* [深度强化学习](https://github.com/nivwusquorum/tensorflow-deepq) \n* [深度强化学习](https://github.com/devsisters/DQN-tensorflow) \n* [深度强化学习](https://github.com/coreylynch/async-rl) \n* [深度强化学习](https://github.com/carpedm20/deep-rl-tensorflow) \n* [深度强化学习](https://github.com/yandexdataschool/Practical_RL)\n* [文本分类 ](https://github.com/dennybritz/cnn-text-classification-tf)\n* [序列建模 ](https://github.com/google/seq2seq) \n* [中文分词](https://github.com/koth/kcws) \n* [基于文本的图像合成](https://github.com/paarthneekhara/text-to-image) \n* [RNN语言建模 ](https://github.com/sherjilozair/char-rnn-tensorflow) \n* [RNN语言建模 ](https://github.com/silicon-valley-data-science/RNN-Tutorial) \n* [神经图灵机](https://github.com/carpedm20/NTM-tensorflow)\n* [语音合成 ](https://github.com/ibab/tensorflow-wavenet) \n* [语音合成](https://github.com/tomlepaine/fast-wavenet) \n* [语音识别](https://github.com/buriburisuri/speech-to-text-wavenet) \n* [语音识别](https://github.com/pannous/tensorflow-speech-recognition)\n* [风格转换 ](https://github.com/anishathalye/neural-style) \n* [风格转换 ](https://github.com/cysmith/neural-style-tf) \n* [运用GAN图像生成](https://github.com/carpedm20/DCGAN-tensorflow) \n* [图像识别](https://github.com/sugyan/tensorflow-mnist)\n* [图像到图像的翻译 ](https://github.com/affinelayer/pix2pix-tensorflow) \n* [图像超分辨](https://github.com/Tetrachrome/subpixel) \n* [人脸识别 ](https://github.com/davidsandberg/facenet) \n* [目标检测 ](https://github.com/TensorBox/TensorBox) \n* [运动识别](https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition) \n* [图像复原](https://github.com/bamos/dcgan-completion.tensorflow) \n* [生成模型](https://github.com/wiseodd/generative-models)\n* [TensorFlow实时debug工具](https://github.com/ericjang/tdb)\n* [TensorFlow在树莓派上的应用](https://github.com/samjabrahams/tensorflow-on-raspberry-pi)\n* [TensorFlow基于R的应用](https://github.com/rstudio/tensorflow)\n* [实时Spark与TensorFlow的输入pipeline](https://github.com/fluxcapacitor/pipeline) \n* [实时Spark与TensorFlow的输入pipeline](https://github.com/yahoo/TensorFlowOnSpark)\n* [caffe与TensorFlow结合](https://github.com/ethereon/caffe-tensorflow)\n* [概率建模](https://github.com/blei-lab/edward)\n*   [YOLO TensorFlow](https://github.com/gliese581gg/YOLO_tensorflow)  — 实现 “YOLO：实时对象检测”\n*   [Magenta](https://github.com/tensorflow/magenta)  — 音乐和艺术的生成与机器智能（研究项目）\n\n## 4. 与 TensorFlow 有关的库\n*   [Scikit Flow (TF Learn)](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/learn/python/learn) — 深度/机器学习的简化接口（现在是 TensorFlow 的一部分）\n*   [tensorflow.rb](https://github.com/somaticio/tensorflow.rb) — 使用 SWIG 用于 Ruby 的 TensorFlow 本地接口\n*   [tflearn](https://github.com/tflearn/tflearn) — 深度学习库，具有更高级别的 API\n*   [TensorFlow-Slim](https://github.com/tensorflow/models/tree/master/inception/inception/slim) — 在 TensorFlow 中定义、训练和评估模型的轻量级库\n*   [TensorFrames](https://github.com/tjhunter/tensorframes) — Apache Spark 的 TensorFlow 绑定，Apache Spark 上 DataFrames 的 Tensorflow 包裹器\n*   [caffe-tensorflow](https://github.com/ethereon/caffe-tensorflow) — 将 Caffe 模型转换为 TensorFlow 格式\n*   [keras](http://keras.io/) — 用于 TensorFlow 和 Theano 的最小、模块化深度学习库\n*   [SyntaxNet: Neural Models of Syntax](https://github.com/tensorflow/models/tree/master/syntaxnet) — TensorFlow 实现全球标准化中基于过渡的神经网络描述的模型\n\n## 5. 教学视频\n*   [TensorFlow Guide 1](http://bit.ly/1OX8s8Y) — TensorFlow 安装和使用指南 1\n*   [TensorFlow Guide 2](http://bit.ly/1R27Ki9) — TensorFlow 安装和使用指南 2\n*   [TensorFlow Basic Usage](http://bit.ly/1TCNmEY) — 基本使用指南\n*   [TensorFlow Deep MNIST for Experts](http://bit.ly/1L9IfJx) — 深入了解 MNIST\n*   [TensorFlow Udacity Deep Learning](https://www.youtube.com/watch?v=ReaxoSIM5XQ) — 在具有 1Gb 数据的 Cloud 9 在线服务上免费安装 TensorFlow 的基本步骤\n*   [为什么 Google 希望每个人都有权访问 TensorFlow](http://video.foxnews.com/v/4611174773001/why-google-wants-everyone-to-have-access-to-tensorflow/?#sp=show-clips)\n*   [2016/1/19 TensorFlow 硅谷见面会](http://blog.altoros.com/videos-from-tensorflow-silicon-valley-meetup-january-19-2016.html)\n*   [2016/1/21 TensorFlow 硅谷见面会](http://blog.altoros.com/videos-from-tensorflow-seattle-meetup-jan-21-2016.html)\n*   [Stanford CS224d Lecture 7 - Introduction to TensorFlow, 19th Apr 2016](https://www.youtube.com/watch?v=L8Y2_Cq2X5s\u0026index=7\u0026list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam)  — CS224d 用于自然语言处理的深度学习\n*   [Diving into Machine Learning through TensorFlow](https://youtu.be/GZBIPwdGtkk?list=PLBkISg6QfSX9HL6us70IBs9slFciFFa4W) — 通过 TensorFlow 进入机器学习，2016 Pycon 大会\n*   [Large Scale Deep Learning with TensorFlow](https://youtu.be/XYwIDn00PAo)  — Jeff Dean Spark Summit 2016 主题演讲\n*   [Tensorflow and deep learning - without at PhD](https://www.youtube.com/watch?v=vq2nnJ4g6N0) — TensorFlow 和 深度学习 （by Martin Görner）\n\n\n## 6. 论文/文献\n\n*   [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](http://download.tensorflow.org/paper/whitepaper2015.pdf) — 介绍了 TensorFlow 接口以及在 Google 上构建的该接口的实现\n*   [Comparative Study of Deep Learning Software Frameworks](http://arxiv.org/abs/1511.06435) — 该研究在几种类型的深度学习架构上进行，我们评估上述框架在单个机器上用于（多线程）CPU 和 GPU（Nvidia Titan X）设置时的性能\n*   [Distributed TensorFlow with MPI](http://arxiv.org/abs/1603.02339) — 在本文中，我们对最近提出的 Google TensorFlow 使用消息传递接口（MPI）在大规模集群上执行进行扩展\n*   [Globally Normalized Transition-Based Neural Networks](http://arxiv.org/abs/1603.06042)  — 本文介绍了 SyntaxNet 背后的模型\n*   [TensorFlow: A system for large-scale machine learning](https://arxiv.org/abs/1605.08695)  — 本文介绍了 TensorFlow 数据流模型与现有系统的对比，并展示了引人注目的性能\n\n## 7. 官方公告\n\n*   [TensorFlow: smarter machine learning, for everyone](https://googleblog.blogspot.com/2015/11/tensorflow-smarter-machine-learning-for.html)  — 介绍 TensorFlow\n*   [Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source](http://googleresearch.blogspot.com/2016/05/announcing-syntaxnet-worlds-most.html)  — SyntaxNet 的发布声明，“一个在 TensorFlow 中实现的开源神经网络框架，为自然语言理解系统提供了基础。\n\n## 8. 博客文章\n\n*   [为什么 TensorFlow 会改变游戏的 AI](http://www.somatic.io/blog/why-tensorflow-will-change-the-game-for-ai)\n*   [TensorFlow for Poets](http://petewarden.com/2016/02/28/tensorflow-for-poets) — 完成 TensorFlow 的实现\n*   [Scikit 流简介，简化 TensorFlow 接口](http://terrytangyuan.github.io/2016/03/14/scikit-flow-intro/) — 主要特点说明\n*   [Building Machine Learning Estimator in TensorFlow](http://terrytangyuan.github.io/2016/07/08/understand-and-build-tensorflow-estimator/) — 了解 TensorFlow 的内部学习估计器\n*   [TensorFlow — 不只是用于深度学习](http://terrytangyuan.github.io/2016/08/06/tensorflow-not-just-deep-learning/)\n*   [ indico 机器学习团队对 TensorFlow 的采纳](https://indico.io/blog/indico-tensorflow)\n*   [The Good, Bad, \u0026 Ugly of TensorFlow](https://indico.io/blog/the-good-bad-ugly-of-tensorflow/) — 一份六个月快速演变的调查\n*   [Fizz Buzz in TensorFlow](http://joelgrus.com/2016/05/23/fizz-buzz-in-tensorflow/) — Joel Grus 的一个笑话\n*   [在 TensorFlow 使用 RNNs 的实用指南和未记录的功能](http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/) — 分步指南，在 GitHub 上提供完整的代码示例\n*   [使用 TensorBoard 在 TensorFlow 中可视化图像分类的重新训练](http://maxmelnick.com/2016/07/04/visualizing-tensorflow-retrain.html)\n\n## 9. 社区\n\n*   [Stack Overflow TensorFlow 专区](http://stackoverflow.com/questions/tagged/tensorflow)\n*   [@TensorFlo 推特账号](https://twitter.com/TensorFlo)\n*   [Reddit 的 TensorFlow 版块](https://www.reddit.com/r/tensorflow)\n*   [邮件列表](https://groups.google.com/a/tensorflow.org/forum/#!forum/discuss)\n\n## 10. 书籍\n\n*   [与 TensorFlow 的初次接触](http://www.jorditorres.org/first-contact-with-tensorflow/) — 作者：Jordi Torres，UPC Barcelona Tech 教授，巴塞罗那超级计算中心研究经理和高级顾问\n*   [使用 Python 进行深度学习](https://machinelearningmastery.com/deep-learning-with-python/) — 使用 Keras 在 Theano 和 TensorFlow 上开发深度学习模型（By Jason Brownlee）\n*   [用于机器智能的 TensorFlow](https://bleedingedgepress.com/tensor-flow-for-machine-intelligence/) — 一份完整指南 — 使用 TensorFlow 从图形计算的基础到深度学习模型，并在生产环境中使用它（Bleeding Edge 出版）\n*   [TensorFlow 入门](https://www.packtpub.com/big-data-and-business-intelligence/getting-started-tensorflow) — 使用 Google 的最新数值计算库开始运行，并深入了解您的数据（By Giancarlo Zaccone）\n*   [使用 Scikit-Learn 和 TensorFlow 的实践机器学习](http://shop.oreilly.com/product/0636920052289.do) — 涵盖 ML 基本原理，使用 TensorFlow，最新的 CNN，RNN 和 Autoencoder 架构在多个服务器和 GPU 上训练和部署深度网络，以及强化学习（Deep Q）\n*   [使用 TensorFlow 构建机器学习项目](https://www.packtpub.com/big-data-and-business-intelligence/building-machine-learning-projects-tensorflow) — 本书涵盖了 TensorFlow 中的各种项目，揭示了 TensorFlow 在不同情况下可以做什么。还提供了关于训练模型，机器学习，深度学习和各种使用神经网络的项目。每个项目都是一个有吸引力和有见地的练习，将教你如何使用 TensorFlow，并告诉您如何通过使用 Tensors 来探索数据层。\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fretval%2Faibook","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fretval%2Faibook","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fretval%2Faibook/lists"}