[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-jtoy--awesome-tensorflow":3,"tool-jtoy--awesome-tensorflow":61},[4,18,26,36,44,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":10,"last_commit_at":24,"category_tags":25,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":27,"name":28,"github_repo":29,"description_zh":30,"stars":31,"difficulty_score":32,"last_commit_at":33,"category_tags":34,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",160411,2,"2026-04-18T23:33:24",[14,13,35],"语言模型",{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":32,"last_commit_at":42,"category_tags":43,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",109154,"2026-04-18T11:18:24",[14,15,13],{"id":45,"name":46,"github_repo":47,"description_zh":48,"stars":49,"difficulty_score":32,"last_commit_at":50,"category_tags":51,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[52,13,15,14],"插件",{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":32,"last_commit_at":59,"category_tags":60,"status":17},4721,"markitdown","microsoft\u002Fmarkitdown","MarkItDown 是一款由微软 AutoGen 团队打造的轻量级 Python 工具，专为将各类文件高效转换为 Markdown 格式而设计。它支持 PDF、Word、Excel、PPT、图片（含 OCR）、音频（含语音转录）、HTML 乃至 YouTube 链接等多种格式的解析，能够精准提取文档中的标题、列表、表格和链接等关键结构信息。\n\n在人工智能应用日益普及的今天，大语言模型（LLM）虽擅长处理文本，却难以直接读取复杂的二进制办公文档。MarkItDown 恰好解决了这一痛点，它将非结构化或半结构化的文件转化为模型“原生理解”且 Token 效率极高的 Markdown 格式，成为连接本地文件与 AI 分析 pipeline 的理想桥梁。此外，它还提供了 MCP（模型上下文协议）服务器，可无缝集成到 Claude Desktop 等 LLM 应用中。\n\n这款工具特别适合开发者、数据科学家及 AI 研究人员使用，尤其是那些需要构建文档检索增强生成（RAG）系统、进行批量文本分析或希望让 AI 助手直接“阅读”本地文件的用户。虽然生成的内容也具备一定可读性，但其核心优势在于为机器",93400,"2026-04-06T19:52:38",[52,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":73,"owner_location":73,"owner_email":73,"owner_twitter":72,"owner_website":76,"owner_url":77,"languages":73,"stars":78,"forks":79,"last_commit_at":80,"license":81,"difficulty_score":82,"env_os":83,"env_gpu":84,"env_ram":84,"env_deps":85,"category_tags":89,"github_topics":73,"view_count":32,"oss_zip_url":73,"oss_zip_packed_at":73,"status":17,"created_at":90,"updated_at":91,"faqs":92,"releases":128},9442,"jtoy\u002Fawesome-tensorflow","awesome-tensorflow","TensorFlow - A curated list of dedicated resources http:\u002F\u002Ftensorflow.org","awesome-tensorflow 是一个精心整理的 TensorFlow 资源合集，旨在为开发者提供一站式的深度学习入门与进阶指南。面对 TensorFlow 庞大的生态系统和分散的学习资料，初学者往往难以筛选出高质量的内容，而该列表有效解决了这一痛点，将教程、开源项目、实用库、工具软件、学术论文及社区博客等资源进行了系统化分类。\n\n无论是刚接触深度学习的学生、希望快速落地的工程师，还是从事前沿算法研究的研究人员，都能在这里找到适合自己的学习路径。从基础的 API 使用教程到复杂的模型复现，从树莓派上的轻量级部署到 Android 端的移动应用开发，内容覆盖广泛且注重实战。其独特亮点在于不仅收录了官方文档，还汇聚了全球社区贡献的优质代码示例和最佳实践，如斯坦福大学课程笔记及各类高效工具库，帮助用户避开常见陷阱，提升开发效率。如果你希望在 TensorFlow 领域深耕，这份清单将是不可或缺的导航图。","# Awesome TensorFlow  [![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome)\n\nA curated list of awesome TensorFlow experiments, libraries, and projects. Inspired by awesome-machine-learning.\n\n## What is TensorFlow?\n\nTensorFlow is an open source software library for numerical computation using data flow graphs. In other words, the best way to build deep learning models.\n\nMore info [here](http:\u002F\u002Ftensorflow.org).\n\n\n\n## Table of Contents\n\n\u003C!-- MarkdownTOC depth=4 -->\n- [Tutorials](#github-tutorials)\n- [Models\u002FProjects](#github-projects)\n- [Powered by TensorFlow](#github-powered-by)\n- [Libraries](#libraries)\n- [Tools\u002FUtilities](#tools-utils)\n- [Videos](#video)\n- [Papers](#papers)\n- [Blog posts](#blogs)\n- [Community](#community)\n- [Books](#books)\n\n\u003C!-- \u002FMarkdownTOC -->\n\n\n\u003Ca name=\"github-tutorials\" \u002F>\n\n## Tutorials\n\n* [TensorFlow Tutorial 1](https:\u002F\u002Fgithub.com\u002Fpkmital\u002Ftensorflow_tutorials) - From the basics to slightly more interesting applications of TensorFlow\n* [TensorFlow Tutorial 2](https:\u002F\u002Fgithub.com\u002Fnlintz\u002FTensorFlow-Tutorials) - Introduction to deep learning based on Google's TensorFlow framework. These tutorials are direct ports of Newmu's Theano\n* [TensorFlow Tutorial 3](https:\u002F\u002Fgithub.com\u002FHvass-Labs\u002FTensorFlow-Tutorials) - These tutorials are intended for beginners in Deep Learning and TensorFlow with well-documented code and YouTube videos.\n* [TensorFlow Examples](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples) - TensorFlow tutorials and code examples for beginners\n* [Sungjoon's TensorFlow-101](https:\u002F\u002Fgithub.com\u002Fsjchoi86\u002FTensorflow-101) - TensorFlow tutorials written in Python with Jupyter Notebook\n* [Terry Um’s TensorFlow Exercises](https:\u002F\u002Fgithub.com\u002Fterryum\u002FTensorFlow_Exercises) - Re-create the codes from other TensorFlow examples\n* [Installing TensorFlow on Raspberry Pi 3](https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-on-raspberry-pi) - TensorFlow compiled and running properly on the Raspberry Pi\n* [Classification on time series](https:\u002F\u002Fgithub.com\u002Fguillaume-chevalier\u002FLSTM-Human-Activity-Recognition) - Recurrent Neural Network classification in TensorFlow with LSTM on cellphone sensor data\n* [Getting Started with TensorFlow on Android](https:\u002F\u002Fomid.al\u002Fposts\u002F2017-02-20-Tutorial-Build-Your-First-Tensorflow-Android-App.html) - Build your first TensorFlow Android app\n* [Predict time series](https:\u002F\u002Fgithub.com\u002Fguillaume-chevalier\u002Fseq2seq-signal-prediction) - Learn to use a seq2seq model on simple datasets as an introduction to the vast array of possibilities that this architecture offers\n* [Single Image Random Dot Stereograms](https:\u002F\u002Fgithub.com\u002FMazecreator\u002FTensorFlow-SIRDS) - SIRDS is a means to present 3D data in a 2D image. It allows for scientific data display of a waterfall type plot with no hidden lines due to perspective.\n* [CS20 SI: TensorFlow for DeepLearning Research](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs20si\u002Fsyllabus.html) - Stanford Course about Tensorflow from 2017 - [Syllabus](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs20si\u002Fsyllabus.html) - [Unofficial Videos](https:\u002F\u002Fyoutu.be\u002Fg-EvyKpZjmQ?list=PLSPPwKHXGS2110rEaNH7amFGmaD5hsObs)\n* [TensorFlow World](https:\u002F\u002Fgithub.com\u002Fastorfi\u002FTensorFlow-World) - Concise and ready-to-use TensorFlow tutorials with detailed documentation are provided.\n* [Effective Tensorflow](https:\u002F\u002Fgithub.com\u002Fvahidk\u002FEffectiveTensorflow) - TensorFlow howtos and best practices. Covers the basics as well as advanced topics.\n* [TensorLayer](http:\u002F\u002Ftensorlayer.readthedocs.io\u002Fen\u002Flatest\u002Fuser\u002Ftutorial.html) - Modular implementation for TensorFlow's official tutorials. ([CN](https:\u002F\u002Ftensorlayercn.readthedocs.io\u002Fzh\u002Flatest\u002Fuser\u002Ftutorial.html)).\n* [Understanding The Tensorflow Estimator API](https:\u002F\u002Fwww.lighttag.io\u002Fblog\u002Ftensorflow-estimator-api\u002F) A conceptual overview of the Estimator API, when you'd use it and why. \n* [Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fintroduction-tensorflow) - Introduction to Tensorflow offered by Coursera\n* [Convolutional Neural Networks in TensorFlow](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fconvolutional-neural-networks-tensorflow) - Convolutional Neural Networks in Tensorflow, offered by Coursera\n* [TensorLayerX](https:\u002F\u002Ftensorlayerx.readthedocs.io\u002Fen\u002Flatest\u002Findex.html#user-guide) - Using TensorFlow like PyTorch. ([Api docs](https:\u002F\u002Ftensorlayerx.readthedocs.io\u002Fen\u002Flatest\u002Findex.html#))\n\n\u003Ca name=\"github-projects\" \u002F>\n\n## Models\u002FProjects\n\n* [Tensorflow-Project-Template](https:\u002F\u002Fgithub.com\u002FMrgemy95\u002FTensorflow-Project-Template) - A simple and well-designed template for your tensorflow project.\n* [Domain Transfer Network](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fdtn-tensorflow) - Implementation of Unsupervised Cross-Domain Image Generation\n* [Show, Attend and Tell](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fshow_attend_and_tell) - Attention Based Image Caption Generator\n* [Neural Style](https:\u002F\u002Fgithub.com\u002Fcysmith\u002Fneural-style-tf) Implementation of Neural Style\n* [SRGAN](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Fsrgan) - Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network\n* [Pretty Tensor](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fprettytensor) - Pretty Tensor provides a high level builder API\n* [Neural Style](https:\u002F\u002Fgithub.com\u002Fanishathalye\u002Fneural-style) - An implementation of neural style\n* [AlexNet3D](https:\u002F\u002Fgithub.com\u002Fdenti\u002FAlexNet3D) - An implementations of AlexNet3D. Simple AlexNet model but with 3D convolutional layers (conv3d).\n* [TensorFlow White Paper Notes](https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-white-paper-notes) - Annotated notes and summaries of the TensorFlow white paper, along with SVG figures and links to documentation\n* [NeuralArt](https:\u002F\u002Fgithub.com\u002Fckmarkoh\u002Fneuralart_tensorflow) - Implementation of A Neural Algorithm of Artistic Style\n* [Generative Handwriting Demo using TensorFlow](https:\u002F\u002Fgithub.com\u002Fhardmaru\u002Fwrite-rnn-tensorflow) - An attempt to implement the random handwriting generation portion of Alex Graves' paper\n* [Neural Turing Machine in TensorFlow](https:\u002F\u002Fgithub.com\u002Fcarpedm20\u002FNTM-tensorflow) - implementation of Neural Turing Machine\n* [GoogleNet Convolutional Neural Network Groups Movie Scenes By Setting](https:\u002F\u002Fgithub.com\u002Fagermanidis\u002Fthingscoop) - Search, filter, and describe videos based on objects, places, and other things that appear in them\n* [Neural machine translation between the writings of Shakespeare and modern English using TensorFlow](https:\u002F\u002Fgithub.com\u002Ftokestermw\u002Ftensorflow-shakespeare) - This performs a monolingual translation, going from modern English to Shakespeare and vice-versa.\n* [Chatbot](https:\u002F\u002Fgithub.com\u002FConchylicultor\u002FDeepQA) - Implementation of [\"A neural conversational model\"](http:\u002F\u002Farxiv.org\u002Fabs\u002F1506.05869)\n* [Seq2seq-Chatbot](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Fseq2seq-chatbot) - Chatbot in 200 lines of code\n* [DCGAN](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Fdcgan) - Deep Convolutional Generative Adversarial Networks\n* [GAN-CLS](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Ftext-to-image) -Generative Adversarial Text to Image Synthesis\n* [im2im](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002FUnsup-Im2Im) - Unsupervised Image to Image Translation with Generative Adversarial Networks\n* [Improved CycleGAN](https:\u002F\u002Fgithub.com\u002Fluoxier\u002FCycleGAN_Tensorlayer) - Unpaired Image to Image Translation\n* [DAGAN](https:\u002F\u002Fgithub.com\u002FnebulaV\u002FDAGAN) - Fast Compressed Sensing MRI Reconstruction\n* [Colornet - Neural Network to colorize grayscale images](https:\u002F\u002Fgithub.com\u002Fpavelgonchar\u002Fcolornet) - Neural Network to colorize grayscale images\n* [Neural Caption Generator](https:\u002F\u002Fgithub.com\u002Fjazzsaxmafia\u002Fshow_attend_and_tell.tensorflow) - Implementation of [\"Show and Tell\"](http:\u002F\u002Farxiv.org\u002Fabs\u002F1411.4555)\n* [Neural Caption Generator with Attention](https:\u002F\u002Fgithub.com\u002Fjazzsaxmafia\u002Fshow_attend_and_tell.tensorflow) - Implementation of [\"Show, Attend and Tell\"](http:\u002F\u002Farxiv.org\u002Fabs\u002F1502.03044)\n* [Weakly_detector](https:\u002F\u002Fgithub.com\u002Fjazzsaxmafia\u002FWeakly_detector) - Implementation of [\"Learning Deep Features for Discriminative Localization\"](http:\u002F\u002Fcnnlocalization.csail.mit.edu\u002F)\n* [Dynamic Capacity Networks](https:\u002F\u002Fgithub.com\u002Fjazzsaxmafia\u002Fdcn.tf) - Implementation of [\"Dynamic Capacity Networks\"](http:\u002F\u002Farxiv.org\u002Fabs\u002F1511.07838)\n* [HMM in TensorFlow](https:\u002F\u002Fgithub.com\u002Fdwiel\u002Ftensorflow_hmm) - Implementation of viterbi and forward\u002Fbackward algorithms for HMM\n* [DeepOSM](https:\u002F\u002Fgithub.com\u002Ftrailbehind\u002FDeepOSM) - Train TensorFlow neural nets with OpenStreetMap features and satellite imagery.\n* [DQN-tensorflow](https:\u002F\u002Fgithub.com\u002Fdevsisters\u002FDQN-tensorflow) - TensorFlow implementation of DeepMind's 'Human-Level Control through Deep Reinforcement Learning' with OpenAI Gym by Devsisters.com\n* [Policy Gradient](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Ftensorlayer\u002Fblob\u002Fmaster\u002Fexample\u002Ftutorial_atari_pong.py) - For Playing Atari Ping Pong\n* [Deep Q-Network](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Ftensorlayer\u002Fblob\u002Fmaster\u002Fexample\u002Ftutorial_frozenlake_dqn.py) - For Playing Frozen Lake Game\n* [AC](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Ftensorlayer\u002Fblob\u002Fmaster\u002Fexample\u002Ftutorial_cartpole_ac.py) - Actor Critic for Playing Discrete Action space Game (Cartpole)\n* [A3C](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Ftensorlayer\u002Fblob\u002Fmaster\u002Fexample\u002Ftutorial_bipedalwalker_a3c_continuous_action.py) - Asynchronous Advantage Actor Critic (A3C) for Continuous Action Space (Bipedal Walker)\n* [DAGGER](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002FImitation-Learning-Dagger-Torcs) - For Playing [Gym Torcs](https:\u002F\u002Fgithub.com\u002Fugo-nama-kun\u002Fgym_torcs)\n* [TRPO](https:\u002F\u002Fgithub.com\u002Fjjkke88\u002FRL_toolbox) - For Continuous and Discrete Action Space by\n* [Highway Network](https:\u002F\u002Fgithub.com\u002Ffomorians\u002Fhighway-cnn) - TensorFlow implementation of [\"Training Very Deep Networks\"](http:\u002F\u002Farxiv.org\u002Fabs\u002F1507.06228) with a [blog post](https:\u002F\u002Fmedium.com\u002Fjim-fleming\u002Fhighway-networks-with-tensorflow-1e6dfa667daa#.ndicn1i27)\n* [Hierarchical Attention Networks](https:\u002F\u002Fgithub.com\u002Ftqtg\u002Fhierarchical-attention-networks) - TensorFlow implementation of [\"Hierarchical Attention Networks for Document Classification\"](https:\u002F\u002Fwww.cs.cmu.edu\u002F~hovy\u002Fpapers\u002F16HLT-hierarchical-attention-networks.pdf)\n* [Sentence Classification with CNN](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Fcnn-text-classification-tf) - TensorFlow implementation of [\"Convolutional Neural Networks for Sentence Classification\"](http:\u002F\u002Farxiv.org\u002Fabs\u002F1408.5882) with a [blog post](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F12\u002Fimplementing-a-cnn-for-text-classification-in-tensorflow\u002F)\n* [End-To-End Memory Networks](https:\u002F\u002Fgithub.com\u002Fdomluna\u002Fmemn2n) - Implementation of [End-To-End Memory Networks](http:\u002F\u002Farxiv.org\u002Fabs\u002F1503.08895)\n* [Character-Aware Neural Language Models](https:\u002F\u002Fgithub.com\u002Fcarpedm20\u002Flstm-char-cnn-tensorflow) - TensorFlow implementation of [Character-Aware Neural Language Models](http:\u002F\u002Farxiv.org\u002Fabs\u002F1508.06615)\n* [YOLO TensorFlow ++](https:\u002F\u002Fgithub.com\u002Fthtrieu\u002Fyolotf) - TensorFlow implementation of 'YOLO: Real-Time Object Detection', with training and an actual support for real-time running on mobile devices.\n* [Wavenet](https:\u002F\u002Fgithub.com\u002Fibab\u002Ftensorflow-wavenet) - This is a TensorFlow implementation of the [WaveNet generative neural network architecture](https:\u002F\u002Fdeepmind.com\u002Fblog\u002Fwavenet-generative-model-raw-audio\u002F) for audio generation.\n* [Mnemonic Descent Method](https:\u002F\u002Fgithub.com\u002Ftrigeorgis\u002Fmdm) - Tensorflow implementation of [\"Mnemonic Descent Method: A recurrent process applied for end-to-end face alignment\"](http:\u002F\u002Fibug.doc.ic.ac.uk\u002Fmedia\u002Fuploads\u002Fdocuments\u002Ftrigeorgis2016mnemonic.pdf)\n* [CNN visualization using Tensorflow](https:\u002F\u002Fgithub.com\u002FInFoCusp\u002Ftf_cnnvis) - Tensorflow implementation of [\"Visualizing and Understanding Convolutional Networks\"](https:\u002F\u002Fwww.cs.nyu.edu\u002F~fergus\u002Fpapers\u002FzeilerECCV2014.pdf)\n* [VGAN Tensorflow](https:\u002F\u002Fgithub.com\u002FSingularity42\u002FVGAN-Tensorflow) - Tensorflow implementation for MIT [\"Generating Videos with Scene Dynamics\"](http:\u002F\u002Fcarlvondrick.com\u002Ftinyvideo\u002F) by Vondrick et al.\n* [3D Convolutional Neural Networks in TensorFlow](https:\u002F\u002Fgithub.com\u002Fastorfi\u002F3D-convolutional-speaker-recognition) - Implementation of [\"3D Convolutional Neural Networks for Speaker Verification application\"](https:\u002F\u002Farxiv.org\u002Fabs\u002F1705.09422) in TensorFlow by Torfi et al.\n* [U-Net](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Fu-net-brain-tumor) - For Brain Tumor Segmentation\n* [Spatial Transformer Networks](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002FSpatial-Transformer-Nets) - Learn the Transformation Function \n* [Lip Reading - Cross Audio-Visual Recognition using 3D Architectures in TensorFlow](https:\u002F\u002Fgithub.com\u002Fastorfi\u002Flip-reading-deeplearning) - TensorFlow Implementation of [\"Cross Audio-Visual Recognition in the Wild Using Deep Learning\"](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.05739) by Torfi et al.\n* [Attentive Object Tracking](https:\u002F\u002Fgithub.com\u002Fakosiorek\u002Fhart) - Implementation of [\"Hierarchical Attentive Recurrent Tracking\"](https:\u002F\u002Farxiv.org\u002Fabs\u002F1706.09262)\n* [Holographic Embeddings for Graph Completion and Link Prediction](https:\u002F\u002Fgithub.com\u002Flaxatives\u002FTensorFlow-TransX) - Implementation of [Holographic Embeddings of Knowledge Graphs](http:\u002F\u002Farxiv.org\u002Fabs\u002F1510.04935)\n* [Unsupervised Object Counting](https:\u002F\u002Fgithub.com\u002Fakosiorek\u002Fattend_infer_repeat) - Implementation of [\"Attend, Infer, Repeat\"](https:\u002F\u002Fpapers.nips.cc\u002Fpaper\u002F6230-attend-infer-repeat-fast-scene-understanding-with-generative-models)\n* [Tensorflow FastText](https:\u002F\u002Fgithub.com\u002Fapcode\u002Ftensorflow_fasttext) - A simple embedding based text classifier inspired by Facebook's fastText.\n* [MusicGenreClassification](https:\u002F\u002Fgithub.com\u002Fmlachmish\u002FMusicGenreClassification) - Classify music genre from a 10 second sound stream using a Neural Network.\n* [Kubeflow](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fkubeflow) - Framework for easily using Tensorflow with Kubernetes.\n* [TensorNets](https:\u002F\u002Fgithub.com\u002Ftaehoonlee\u002Ftensornets) - 40+ Popular Computer Vision Models With Pre-trained Weights.\n* [Ladder Network](https:\u002F\u002Fgithub.com\u002Fdivamgupta\u002Fladder_network_keras) - Implementation of Ladder Network for Semi-Supervised Learning in Keras and Tensorflow\n* [TF-Unet](https:\u002F\u002Fgithub.com\u002Fjuniorxsound\u002FTF-Unet) - General purpose U-Network implemented in Keras for image segmentation\n* [Sarus TF2 Models](https:\u002F\u002Fgithub.com\u002Fsarus-tech\u002Ftf2-published-models) - A long list of recent generative models implemented in clean, easy to reuse, Tensorflow 2 code (Plain Autoencoder, VAE, VQ-VAE, PixelCNN, Gated PixelCNN, PixelCNN++, PixelSNAIL, Conditional Neural Processes).\n* [Model Maker](https:\u002F\u002Fwww.tensorflow.org\u002Flite\u002Fguide\u002Fmodel_maker) - A transfer learning library that simplifies the process of training, evaluation and deployment for TensorFlow Lite models (support: Image Classification, Object Detection, Text Classification, BERT Question Answer, Audio Classification, Recommendation etc.; [API reference](https:\u002F\u002Fwww.tensorflow.org\u002Flite\u002Fapi_docs\u002Fpython\u002Ftflite_model_maker)).\n\n\n\u003Ca name=\"github-powered-by\" \u002F>\n\n## Powered by TensorFlow\n\n* [YOLO TensorFlow](https:\u002F\u002Fgithub.com\u002Fgliese581gg\u002FYOLO_tensorflow) - Implementation of 'YOLO : Real-Time Object Detection'\n* [android-yolo](https:\u002F\u002Fgithub.com\u002Fnatanielruiz\u002Fandroid-yolo) - Real-time object detection on Android using the YOLO network, powered by TensorFlow.\n* [Magenta](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmagenta) - Research project to advance the state of the art in machine intelligence for music and art generation\n\n\n\u003Ca name=\"libraries\" \u002F>\n\n## Libraries\n\n* [TensorFlow Estimators](https:\u002F\u002Fwww.tensorflow.org\u002Fguide\u002Festimators) - high-level TensorFlow API that greatly simplifies machine learning programming (originally [tensorflow\u002Fskflow](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fskflow))\n* [R Interface to TensorFlow](https:\u002F\u002Ftensorflow.rstudio.com\u002F) - R interface to TensorFlow APIs, including Estimators, Keras, Datasets, etc.\n* [Lattice](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Flattice) - Implementation of Monotonic Calibrated Interpolated Look-Up Tables in TensorFlow\n* [tensorflow.rb](https:\u002F\u002Fgithub.com\u002Fsomaticio\u002Ftensorflow.rb) - TensorFlow native interface for ruby using SWIG\n* [tflearn](https:\u002F\u002Fgithub.com\u002Ftflearn\u002Ftflearn) - Deep learning library featuring a higher-level API\n* [TensorLayer](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Ftensorlayer) - Deep learning and reinforcement learning library for researchers and engineers\n* [TensorFlow-Slim](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmodels\u002Ftree\u002Fmaster\u002Finception\u002Finception\u002Fslim) - High-level library for defining models\n* [TensorFrames](https:\u002F\u002Fgithub.com\u002Ftjhunter\u002Ftensorframes) - TensorFlow binding for Apache Spark\n* [TensorForce](https:\u002F\u002Fgithub.com\u002Freinforceio\u002Ftensorforce) - TensorForce: A TensorFlow library for applied reinforcement learning\n* [TensorFlowOnSpark](https:\u002F\u002Fgithub.com\u002Fyahoo\u002FTensorFlowOnSpark) - initiative from Yahoo! to enable distributed TensorFlow with Apache Spark.\n* [caffe-tensorflow](https:\u002F\u002Fgithub.com\u002Fethereon\u002Fcaffe-tensorflow) - Convert Caffe models to TensorFlow format\n* [keras](http:\u002F\u002Fkeras.io) - Minimal, modular deep learning library for TensorFlow and Theano\n* [SyntaxNet: Neural Models of Syntax](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmodels\u002Ftree\u002Fmaster\u002Fsyntaxnet) - A TensorFlow implementation of the models described in [Globally Normalized Transition-Based Neural Networks, Andor et al. (2016)](http:\u002F\u002Farxiv.org\u002Fpdf\u002F1603.06042.pdf)\n* [keras-js](https:\u002F\u002Fgithub.com\u002Ftranscranial\u002Fkeras-js) - Run Keras models (tensorflow backend) in the browser, with GPU support\n* [NNFlow](https:\u002F\u002Fgithub.com\u002Fwelschma\u002FNNFlow) - Simple framework allowing to read-in ROOT NTuples by converting them to a Numpy array and then use them in Google Tensorflow.\n* [Sonnet](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Fsonnet) - Sonnet is DeepMind's library built on top of TensorFlow for building complex neural networks.\n* [tensorpack](https:\u002F\u002Fgithub.com\u002Fppwwyyxx\u002Ftensorpack) - Neural Network Toolbox on TensorFlow focusing on training speed and on large datasets.\n* [tf-encrypted](https:\u002F\u002Fgithub.com\u002Fmortendahl\u002Ftf-encrypted) - Layer on top of TensorFlow for doing machine learning on encrypted data\n* [pytorch2keras](https:\u002F\u002Fgithub.com\u002Fnerox8664\u002Fpytorch2keras) - Convert PyTorch models to Keras (with TensorFlow backend) format\n* [gluon2keras](https:\u002F\u002Fgithub.com\u002Fstjordanis\u002Fgluon2keras) - Convert Gluon models to Keras (with TensorFlow backend) format\n* [TensorIO](https:\u002F\u002Fdoc-ai.github.io\u002Ftensorio\u002F) - Lightweight, cross-platform library for deploying TensorFlow Lite models to mobile devices. \n* [StellarGraph](https:\u002F\u002Fgithub.com\u002Fstellargraph\u002Fstellargraph) - Machine Learning on Graphs, a Python library for machine learning on graph-structured (network-structured) data.\n* [DeepBay](https:\u002F\u002Fgithub.com\u002FElPapi42\u002FDeepBay) - High-Level Keras Complement for implement common architectures stacks, served as easy to use plug-n-play modules\n* [Tensorflow-Probability](https:\u002F\u002Fwww.tensorflow.org\u002Fprobability) - Probabilistic programming built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware.\n* [TensorLayerX](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002FTensorLayerX) - TensorLayerX: A Unified Deep Learning Framework for All Hardwares, Backends and OS, including TensorFlow.\n* [Txeo](https:\u002F\u002Fgithub.com\u002Frdabra\u002Ftxeo) - A modern C++ wrapper for TensorFlow.\n\n\u003Ca name=\"tools-utils\" \u002F>\n\n## Tools\u002FUtilities\n\n* [Speedster](https:\u002F\u002Fgithub.com\u002Fnebuly-ai\u002Fnebullvm\u002Ftree\u002Fmain\u002Fapps\u002Faccelerate\u002Fspeedster) - Automatically apply SOTA optimization techniques to achieve the maximum inference speed-up on your hardware.\n* [Guild AI](https:\u002F\u002Fguild.ai) - Task runner and package manager for TensorFlow\n* [ML Workspace](https:\u002F\u002Fgithub.com\u002Fml-tooling\u002Fml-workspace) - All-in-one web IDE for machine learning and data science. Combines Tensorflow, Jupyter, VS Code, Tensorboard, and many other tools\u002Flibraries into one Docker image.\n* [create-tf-app](https:\u002F\u002Fgithub.com\u002Fradi-cho\u002Fcreate-tf-app) - Project builder command line tool for Tensorflow covering environment management, linting, and logging.\n\n\u003Ca name=\"video\" \u002F>\n\n## Videos\n\n* [TensorFlow Guide 1](http:\u002F\u002Fbit.ly\u002F1OX8s8Y) - A guide to installation and use\n* [TensorFlow Guide 2](http:\u002F\u002Fbit.ly\u002F1R27Ki9) - Continuation of first video\n* [TensorFlow Basic Usage](http:\u002F\u002Fbit.ly\u002F1TCNmEY) - A guide going over basic usage\n* [TensorFlow Deep MNIST for Experts](http:\u002F\u002Fbit.ly\u002F1L9IfJx) - Goes over Deep MNIST\n* [TensorFlow Udacity Deep Learning](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ReaxoSIM5XQ) - Basic steps to install TensorFlow for free on the Cloud 9 online service with 1Gb of data\n* [Why Google wants everyone to have access to TensorFlow](http:\u002F\u002Fvideo.foxnews.com\u002Fv\u002F4611174773001\u002Fwhy-google-wants-everyone-to-have-access-to-tensorflow\u002F?#sp=show-clips)\n* [Videos from TensorFlow Silicon Valley Meet Up 1\u002F19\u002F2016](http:\u002F\u002Fblog.altoros.com\u002Fvideos-from-tensorflow-silicon-valley-meetup-january-19-2016.html)\n* [Videos from TensorFlow Silicon Valley Meet Up 1\u002F21\u002F2016](http:\u002F\u002Fblog.altoros.com\u002Fvideos-from-tensorflow-seattle-meetup-jan-21-2016.html)\n* [Stanford CS224d Lecture 7 - Introduction to TensorFlow, 19th Apr 2016](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=L8Y2_Cq2X5s&index=7&list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam) - CS224d Deep Learning for Natural Language Processing by Richard Socher\n* [Diving into Machine Learning through TensorFlow](https:\u002F\u002Fyoutu.be\u002FGZBIPwdGtkk?list=PLBkISg6QfSX9HL6us70IBs9slFciFFa4W) - Pycon 2016 Portland Oregon, [Slide](https:\u002F\u002Fstorage.googleapis.com\u002Famy-jo\u002Ftalks\u002Ftf-workshop.pdf) & [Code](https:\u002F\u002Fgithub.com\u002Famygdala\u002Ftensorflow-workshop) by Julia Ferraioli, Amy Unruh, Eli Bixby\n* [Large Scale Deep Learning with TensorFlow](https:\u002F\u002Fyoutu.be\u002FXYwIDn00PAo) - Spark Summit 2016 Keynote by Jeff Dean\n* [Tensorflow and deep learning - without at PhD](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vq2nnJ4g6N0) -  by Martin Görner\n* [Tensorflow and deep learning - without at PhD, Part 2 (Google Cloud Next '17)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fTUwdXUFfI8) -  by Martin Görner\n* [Image recognition in Go using TensorFlow](https:\u002F\u002Fyoutu.be\u002FP8MZ1Z2LHrw) -  by Alex Pliutau\n\n\n\n\u003Ca name=\"papers\" \u002F>\n\n## Papers\n\n* [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](http:\u002F\u002Fdownload.tensorflow.org\u002Fpaper\u002Fwhitepaper2015.pdf) - This paper describes the TensorFlow interface and an implementation of that interface that we have built at Google\n* [TensorFlow Estimators: Managing Simplicity vs. Flexibility in High-Level Machine Learning Frameworks](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1708.02637.pdf)\n* [TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.04251)\n* [Comparative Study of Deep Learning Software Frameworks](http:\u002F\u002Farxiv.org\u002Fabs\u002F1511.06435) - The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings\n* [Distributed TensorFlow with MPI](http:\u002F\u002Farxiv.org\u002Fabs\u002F1603.02339) - In this paper, we extend recently proposed Google TensorFlow for execution on large scale clusters using Message Passing Interface (MPI)\n* [Globally Normalized Transition-Based Neural Networks](http:\u002F\u002Farxiv.org\u002Fabs\u002F1603.06042) - This paper describes the models behind [SyntaxNet](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmodels\u002Ftree\u002Fmaster\u002Fsyntaxnet).\n* [TensorFlow: A system for large-scale machine learning](https:\u002F\u002Farxiv.org\u002Fabs\u002F1605.08695) - This paper describes the TensorFlow dataflow model in contrast to existing systems and demonstrate the compelling performance\n* [TensorLayer: A Versatile Library for Efficient Deep Learning Development](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08551) - This paper describes a versatile Python library that aims at helping researchers and engineers efficiently develop deep learning systems. (Winner of The Best Open Source Software Award of ACM MM 2017)\n\n\u003Ca name=\"blogs\" \u002F>\n\n## Official announcements\n\n* [TensorFlow: smarter machine learning, for everyone](https:\u002F\u002Fgoogleblog.blogspot.com\u002F2015\u002F11\u002Ftensorflow-smarter-machine-learning-for.html) - An introduction to TensorFlow\n* [Announcing SyntaxNet: The World’s Most Accurate Parser Goes Open Source](http:\u002F\u002Fgoogleresearch.blogspot.com\u002F2016\u002F05\u002Fannouncing-syntaxnet-worlds-most.html) - Release of SyntaxNet, \"an open-source neural network framework implemented in TensorFlow that provides a foundation for Natural Language Understanding systems.\n\n## Blog posts\n* [Official Tensorflow Blog](http:\u002F\u002Fblog.tensorflow.org\u002F)\n* [Why TensorFlow will change the Game for AI](https:\u002F\u002Farchive.fo\u002Fo9asj)\n* [TensorFlow for Poets](http:\u002F\u002Fpetewarden.com\u002F2016\u002F02\u002F28\u002Ftensorflow-for-poets) - Goes over the implementation of TensorFlow\n* [Introduction to Scikit Flow - Simplified Interface to TensorFlow](http:\u002F\u002Fterrytangyuan.github.io\u002F2016\u002F03\u002F14\u002Fscikit-flow-intro\u002F) - Key Features Illustrated\n* [Building Machine Learning Estimator in TensorFlow](http:\u002F\u002Fterrytangyuan.github.io\u002F2016\u002F07\u002F08\u002Funderstand-and-build-tensorflow-estimator\u002F) - Understanding the Internals of TensorFlow Learn Estimators\n* [TensorFlow - Not Just For Deep Learning](http:\u002F\u002Fterrytangyuan.github.io\u002F2016\u002F08\u002F06\u002Ftensorflow-not-just-deep-learning\u002F)\n* [The indico Machine Learning Team's take on TensorFlow](https:\u002F\u002Findico.io\u002Fblog\u002Findico-tensorflow)\n* [The Good, Bad, & Ugly of TensorFlow](https:\u002F\u002Findico.io\u002Fblog\u002Fthe-good-bad-ugly-of-tensorflow\u002F) - A survey of six months rapid evolution (+ tips\u002Fhacks and code to fix the ugly stuff), Dan Kuster at Indico, May 9, 2016\n* [Fizz Buzz in TensorFlow](http:\u002F\u002Fjoelgrus.com\u002F2016\u002F05\u002F23\u002Ffizz-buzz-in-tensorflow\u002F) - A joke by Joel Grus\n* [RNNs In TensorFlow, A Practical Guide And Undocumented Features](http:\u002F\u002Fwww.wildml.com\u002F2016\u002F08\u002Frnns-in-tensorflow-a-practical-guide-and-undocumented-features\u002F) - Step-by-step guide with full code examples on GitHub.\n* [Using TensorBoard to Visualize Image Classification Retraining in TensorFlow](http:\u002F\u002Fmaxmelnick.com\u002F2016\u002F07\u002F04\u002Fvisualizing-tensorflow-retrain.html)\n* [TFRecords Guide](http:\u002F\u002Fwarmspringwinds.github.io\u002Ftensorflow\u002Ftf-slim\u002F2016\u002F12\u002F21\u002Ftfrecords-guide\u002F) semantic segmentation and handling the TFRecord file format.\n* [TensorFlow Android Guide](https:\u002F\u002Fblog.mindorks.com\u002Fandroid-tensorflow-machine-learning-example-ff0e9b2654cc) - Android TensorFlow Machine Learning Example.\n* [TensorFlow Optimizations on Modern Intel® Architecture](https:\u002F\u002Fsoftware.intel.com\u002Fen-us\u002Farticles\u002Ftensorflow-optimizations-on-modern-intel-architecture) - Introduces TensorFlow optimizations on Intel® Xeon® and Intel® Xeon Phi™ processor-based platforms based on an Intel\u002FGoogle collaboration.\n* [Coca-Cola's Image Recognition App](https:\u002F\u002Fdevelopers.googleblog.com\u002F2017\u002F09\u002Fhow-machine-learning-with-tensorflow.html) Coca-Cola's product code image recognizing neural network with user input feedback loop.\n* [How Does The TensorFlow Work](https:\u002F\u002Fwww.letslearnai.com\u002F2018\u002F02\u002F02\u002Fhow-does-the-machine-learning-library-tensorflow-work.html) How Does The Machine Learning Library TensorFlow Work?\n\n\n\u003Ca name=\"community\" \u002F>\n\n## Community\n\n* [Stack Overflow](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Ftensorflow)\n* [@TensorFlow on Twitter](https:\u002F\u002Ftwitter.com\u002Ftensorflow)\n* [Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Ftensorflow)\n* [Mailing List](https:\u002F\u002Fgroups.google.com\u002Fa\u002Ftensorflow.org\u002Fforum\u002F#!forum\u002Fdiscuss)\n\n\n\u003Ca name=\"books\" \u002F>\n\n## Books\n\n* [Machine Learning with TensorFlow 2nd edition]([http:\u002F\u002Ftensorflowbook.com](https:\u002F\u002Fgithub.com\u002Fchrismattmann\u002FMLwithTensorFlow2ed)) by [Dr. Chris A. Mattmann](http:\u002F\u002Fgithub.com\u002Fchrismattmann\u002F), Chief Data and Artificial Intelligence Officer at UCLA and author also of [Tika in Action](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Ftika-in-action). This book makes the math-heavy topic of AI and ML approachable and practicle to a newcomer. Updated to Tensorflow2 and the latest version of this book.\n* [First Contact with TensorFlow](http:\u002F\u002Fwww.jorditorres.org\u002Ffirst-contact-with-tensorflow\u002F) by Jordi Torres, professor at UPC Barcelona Tech and a research manager and senior advisor at Barcelona Supercomputing Center\n* [Deep Learning with Python](https:\u002F\u002Fmachinelearningmastery.com\u002Fdeep-learning-with-python\u002F) - Develop Deep Learning Models on Theano and TensorFlow Using Keras by Jason Brownlee\n* [TensorFlow for Machine Intelligence](https:\u002F\u002Fbleedingedgepress.com\u002Ftensor-flow-for-machine-intelligence\u002F) - Complete guide to use TensorFlow from the basics of graph computing, to deep learning models to using it in production environments - Bleeding Edge Press\n* [Getting Started with TensorFlow](https:\u002F\u002Fwww.packtpub.com\u002Fbig-data-and-business-intelligence\u002Fgetting-started-tensorflow) - Get up and running with the latest numerical computing library by Google and dive deeper into your data, by Giancarlo Zaccone\n* [Hands-On Machine Learning with Scikit-Learn and TensorFlow](http:\u002F\u002Fshop.oreilly.com\u002Fproduct\u002F0636920052289.do) – by Aurélien Geron, former lead of the YouTube video classification team. Covers ML fundamentals, training and deploying deep nets across multiple servers and GPUs using TensorFlow, the latest CNN, RNN and Autoencoder architectures, and Reinforcement Learning (Deep Q).\n* [Building Machine Learning Projects with Tensorflow](https:\u002F\u002Fwww.packtpub.com\u002Fbig-data-and-business-intelligence\u002Fbuilding-machine-learning-projects-tensorflow) – by Rodolfo Bonnin. This book covers various projects in TensorFlow that expose what can be done with TensorFlow in different scenarios. The book provides projects on training models, machine learning, deep learning, and working with various neural networks. Each project is an engaging and insightful exercise that will teach you how to use TensorFlow and show you how layers of data can be explored by working with Tensors.\n* [Deep Learning using TensorLayer](http:\u002F\u002Fwww.broadview.com.cn\u002Fbook\u002F5059) - by Hao Dong et al. This book covers both deep learning and the implementation by using TensorFlow and TensorLayer.\n* [TensorFlow 2.0 in Action](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Ftensorflow-in-action) - by Thushan Ganegedara. This practical guide to building deep learning models with the new features of TensorFlow 2.0 is filled with engaging projects, simple language, and coverage of the latest algorithms.\n* [Probabilistic Programming and Bayesian Methods for Hackers](https:\u002F\u002Fgithub.com\u002FCamDavidsonPilon\u002FProbabilistic-Programming-and-Bayesian-Methods-for-Hackers) - by Cameron Davidson-Pilon. Introduction to Bayesian methods and probabilistic graphical models using tensorflow-probability (and, alternatively PyMC2\u002F3). \n\n\n\n\u003Ca name=\"contributions\" \u002F>\n\n## Contributions\n\nYour contributions are always welcome!\n\nIf you want to contribute to this list (please do), send me a pull request or contact me [@jtoy](https:\u002F\u002Ftwitter.com\u002Fjtoy)\nAlso, if you notice that any of the above listed repositories should be deprecated, due to any of the following reasons:\n\n* Repository's owner explicitly say that \"this library is not maintained\".\n* Not committed for long time (2~3 years).\n\nMore info on the [guidelines](https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow\u002Fblob\u002Fmaster\u002Fcontributing.md)\n\n\n\u003Ca name=\"credits\" \u002F>\n\n## Credits\n\n* Some of the python libraries were cut-and-pasted from [vinta](https:\u002F\u002Fgithub.com\u002Fvinta\u002Fawesome-python)\n* The few go reference I found where pulled from [this page](https:\u002F\u002Fcode.google.com\u002Fp\u002Fgo-wiki\u002Fwiki\u002FProjects#Machine_Learning)\n\n","# 令人惊叹的 TensorFlow  [![Awesome](https:\u002F\u002Fcdn.rawgit.com\u002Fsindresorhus\u002Fawesome\u002Fd7305f38d29fed78fa85652e3a63e154dd8e8829\u002Fmedia\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome)\n\n一份精心整理的、关于 TensorFlow 的优秀实验、库和项目的列表。灵感来源于 awesome-machine-learning。\n\n## 什么是 TensorFlow？\n\nTensorFlow 是一个开源软件库，用于通过数据流图进行数值计算。换句话说，它是构建深度学习模型的最佳方式。\n\n更多信息请见 [这里](http:\u002F\u002Ftensorflow.org)。\n\n\n\n## 目录\n\n\u003C!-- MarkdownTOC depth=4 -->\n- [教程](#github-tutorials)\n- [模型\u002F项目](#github-projects)\n- [由 TensorFlow 驱动](#github-powered-by)\n- [库](#libraries)\n- [工具\u002F实用程序](#tools-utils)\n- [视频](#video)\n- [论文](#papers)\n- [博客文章](#blogs)\n- [社区](#community)\n- [书籍](#books)\n\n\u003C!-- \u002FMarkdownTOC -->\n\n\n\u003Ca name=\"github-tutorials\" \u002F>\n\n## 教程\n\n* [TensorFlow 教程 1](https:\u002F\u002Fgithub.com\u002Fpkmital\u002Ftensorflow_tutorials) - 从基础到稍微复杂一些的 TensorFlow 应用\n* [TensorFlow 教程 2](https:\u002F\u002Fgithub.com\u002Fnlintz\u002FTensorFlow-Tutorials) - 基于 Google 的 TensorFlow 框架的深度学习入门教程。这些教程直接移植自 Newmu 的 Theano。\n* [TensorFlow 教程 3](https:\u002F\u002Fgithub.com\u002FHvass-Labs\u002FTensorFlow-Tutorials) - 这些教程面向深度学习和 TensorFlow 的初学者，代码有详细注释，并配有 YouTube 视频。\n* [TensorFlow 示例](https:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples) - 面向初学者的 TensorFlow 教程和代码示例\n* [Sungjoon 的 TensorFlow-101](https:\u002F\u002Fgithub.com\u002Fsjchoi86\u002FTensorflow-101) - 用 Python 和 Jupyter Notebook 编写的 TensorFlow 教程\n* [Terry Um 的 TensorFlow 练习](https:\u002F\u002Fgithub.com\u002Fterryum\u002FTensorFlow_Exercises) - 重新实现其他 TensorFlow 示例中的代码\n* [在树莓派 3 上安装 TensorFlow](https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-on-raspberry-pi) - 在树莓派上成功编译并运行 TensorFlow\n* [时间序列分类](https:\u002F\u002Fgithub.com\u002Fguillaume-chevalier\u002FLSTM-Human-Activity-Recognition) - 使用 LSTM 对手机传感器数据进行循环神经网络分类\n* [在 Android 上开始使用 TensorFlow](https:\u002F\u002Fomid.al\u002Fposts\u002F2017-02-20-Tutorial-Build-Your-First-Tensorflow-Android-App.html) - 构建你的第一个 TensorFlow Android 应用\n* [预测时间序列](https:\u002F\u002Fgithub.com\u002Fguillaume-chevalier\u002Fseq2seq-signal-prediction) - 学习如何在简单数据集上使用 seq2seq 模型，以此作为了解该架构广阔可能性的入门\n* [单张随机点立体图](https:\u002F\u002Fgithub.com\u002FMazecreator\u002FTensorFlow-SIRDS) - SIRDS 是一种将 3D 数据以 2D 图像形式呈现的方法。它允许以瀑布图的形式展示科学数据，且不会因视角而出现隐藏线条。\n* [CS20 SI：用于深度学习研究的 TensorFlow](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs20si\u002Fsyllabus.html) - 斯坦福大学 2017 年关于 TensorFlow 的课程 - [课程大纲](http:\u002F\u002Fweb.stanford.edu\u002Fclass\u002Fcs20si\u002Fsyllabus.html) - [非官方视频](https:\u002F\u002Fyoutu.be\u002Fg-EvyKpZjmQ?list=PLSPPwKHXGS2110rEaNH7amFGmaD5hsObs)\n* [TensorFlow 世界](https:\u002F\u002Fgithub.com\u002Fastorfi\u002FTensorFlow-World) - 提供简洁易用、文档详尽的 TensorFlow 教程。\n* [高效 TensorFlow](https:\u002F\u002Fgithub.com\u002Fvahidk\u002FEffectiveTensorflow) - TensorFlow 使用指南及最佳实践。涵盖基础知识以及高级主题。\n* [TensorLayer](http:\u002F\u002Ftensorlayer.readthedocs.io\u002Fen\u002Flatest\u002Fuser\u002Ftutorial.html) - 为 TensorFlow 官方教程提供的模块化实现。([中文版](https:\u002F\u002Ftensorlayercn.readthedocs.io\u002Fzh\u002Flatest\u002Fuser\u002Ftutorial.html))。\n* [理解 TensorFlow Estimator API](https:\u002F\u002Fwww.lighttag.io\u002Fblog\u002Ftensorflow-estimator-api\u002F) 对 Estimator API 的概念性概述，包括何时以及为何使用它。\n* [人工智能、机器学习和深度学习中的 TensorFlow 入门](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fintroduction-tensorflow) - Coursera 提供的 TensorFlow 入门课程\n* [TensorFlow 中的卷积神经网络](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fconvolutional-neural-networks-tensorflow) - Coursera 提供的 TensorFlow 卷积神经网络课程\n* [TensorLayerX](https:\u002F\u002Ftensorlayerx.readthedocs.io\u002Fen\u002Flatest\u002Findex.html#user-guide) - 让 TensorFlow 的使用体验更接近 PyTorch。([API 文档](https:\u002F\u002Ftensorlayerx.readthedocs.io\u002Fen\u002Flatest\u002Findex.html#))\n\n\u003Ca name=\"github-projects\" \u002F>\n\n## 模型\u002F项目\n\n* [TensorFlow项目模板](https:\u002F\u002Fgithub.com\u002FMrgemy95\u002FTensorflow-Project-Template) - 一个简单且设计良好的TensorFlow项目模板。\n* [领域迁移网络](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fdtn-tensorflow) - 无监督跨域图像生成的实现\n* [Show, Attend and Tell](https:\u002F\u002Fgithub.com\u002Fyunjey\u002Fshow_attend_and_tell) - 基于注意力机制的图像字幕生成器\n* [神经风格迁移](https:\u002F\u002Fgithub.com\u002Fcysmith\u002Fneural-style-tf) 神经风格迁移的实现\n* [SRGAN](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Fsrgan) - 使用生成对抗网络实现照片级真实感单幅图像超分辨率\n* [Pretty Tensor](https:\u002F\u002Fgithub.com\u002Fgoogle\u002Fprettytensor) - Pretty Tensor提供了一个高层次的构建API\n* [神经风格迁移](https:\u002F\u002Fgithub.com\u002Fanishathalye\u002Fneural-style) - 神经风格迁移的实现\n* [AlexNet3D](https:\u002F\u002Fgithub.com\u002Fdenti\u002FAlexNet3D) - AlexNet3D的实现。这是一个简单的AlexNet模型，但使用了三维卷积层（conv3d）。\n* [TensorFlow白皮书笔记](https:\u002F\u002Fgithub.com\u002Fsamjabrahams\u002Ftensorflow-white-paper-notes) - TensorFlow白皮书的注释和摘要，附带SVG图表和文档链接\n* [NeuralArt](https:\u002F\u002Fgithub.com\u002Fckmarkoh\u002Fneuralart_tensorflow) - 艺术风格神经算法的实现\n* [使用TensorFlow的生成式手写演示](https:\u002F\u002Fgithub.com\u002Fhardmaru\u002Fwrite-rnn-tensorflow) - 尝试实现Alex Graves论文中随机手写生成的部分\n* [TensorFlow中的神经图灵机](https:\u002F\u002Fgithub.com\u002Fcarpedm20\u002FNTM-tensorflow) - 神经图灵机的实现\n* [GoogleNet卷积神经网络按场景对电影片段进行分组](https:\u002F\u002Fgithub.com\u002Fagermanidis\u002Fthingscoop) - 根据视频中出现的物体、地点等信息搜索、筛选和描述视频\n* [使用TensorFlow在莎士比亚作品与现代英语之间进行神经机器翻译](https:\u002F\u002Fgithub.com\u002Ftokestermw\u002Ftensorflow-shakespeare) - 这是一个单语翻译任务，可以在现代英语和莎士比亚英语之间相互转换。\n* [聊天机器人](https:\u002F\u002Fgithub.com\u002FConchylicultor\u002FDeepQA) - “一种神经对话模型”的实现\n* [Seq2seq聊天机器人](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Fseq2seq-chatbot) - 用200行代码实现的聊天机器人\n* [DCGAN](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Fdcgan) - 深度卷积生成对抗网络\n* [GAN-CLS](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Ftext-to-image) - 生成对抗文本到图像合成\n* [im2im](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002FUnsup-Im2Im) - 使用生成对抗网络进行无监督图像到图像转换\n* [改进的CycleGAN](https:\u002F\u002Fgithub.com\u002Fluoxier\u002FCycleGAN_Tensorlayer) - 非配对图像到图像转换\n* [DAGAN](https:\u002F\u002Fgithub.com\u002FnebulaV\u002FDAGAN) - 快速压缩感知MRI重建\n* [Colornet - 用于将灰度图像上色的神经网络](https:\u002F\u002Fgithub.com\u002Fpavelgonchar\u002Fcolornet) - 用于将灰度图像上色的神经网络\n* [神经字幕生成器](https:\u002F\u002Fgithub.com\u002Fjazzsaxmafia\u002Fshow_attend_and_tell.tensorflow) - “Show and Tell”（展示与讲述）的实现\n* [带有注意力机制的神经字幕生成器](https:\u002F\u002Fgithub.com\u002Fjazzsaxmafia\u002Fshow_attend_and_tell.tensorflow) - “Show, Attend and Tell”（展示、关注与讲述）的实现\n* [弱监督检测器](https:\u002F\u002Fgithub.com\u002Fjazzsaxmafia\u002FWeakly_detector) - “学习判别性定位的深度特征”的实现\n* [动态容量网络](https:\u002F\u002Fgithub.com\u002Fjazzsaxmafia\u002Fdcn.tf) - “动态容量网络”的实现\n* [TensorFlow中的隐马尔可夫模型](https:\u002F\u002Fgithub.com\u002Fdwiel\u002Ftensorflow_hmm) - HMM的维特比算法和前向\u002F后向算法的实现\n* [DeepOSM](https:\u002F\u002Fgithub.com\u002Ftrailbehind\u002FDeepOSM) - 使用OpenStreetMap特征和卫星图像训练TensorFlow神经网络。\n* [DQN-tensorflow](https:\u002F\u002Fgithub.com\u002Fdevsisters\u002FDQN-tensorflow) - Devsisters.com基于OpenAI Gym实现的DeepMind“通过深度强化学习达到人类水平控制”的TensorFlow版本\n* [策略梯度](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Ftensorlayer\u002Fblob\u002Fmaster\u002Fexample\u002Ftutorial_atari_pong.py) - 用于玩Atari乒乓球游戏\n* [深度Q网络](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Ftensorlayer\u002Fblob\u002Fmaster\u002Fexample\u002Ftutorial_frozenlake_dqn.py) - 用于玩Frozen Lake游戏\n* [AC](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Ftensorlayer\u002Fblob\u002Fmaster\u002Fexample\u002Ftutorial_cartpole_ac.py) - 用于玩离散动作空间游戏（Cartpole）的Actor-Critic\n* [A3C](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Ftensorlayer\u002Fblob\u002Fmaster\u002Fexample\u002Ftutorial_bipedalwalker_a3c_continuous_action.py) - 用于连续动作空间游戏（双足行走者）的异步优势演员评论家（A3C）\n* [DAGGER](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002FImitation-Learning-Dagger-Torcs) - 用于玩[Gym Torcs](https:\u002F\u002Fgithub.com\u002Fugo-nama-kun\u002Fgym_torcs)\n* [TRPO](https:\u002F\u002Fgithub.com\u002Fjjkke88\u002FRL_toolbox) - 适用于连续和离散动作空间\n* [高速公路网络](https:\u002F\u002Fgithub.com\u002Ffomorians\u002Fhighway-cnn) - TensorFlow实现的“训练非常深的网络”，并配有[博客文章](https:\u002F\u002Fmedium.com\u002Fjim-fleming\u002Fhighway-networks-with-tensorflow-1e6dfa667daa#.ndicn1i27)\n* [层次化注意力网络](https:\u002F\u002Fgithub.com\u002Ftqtg\u002Fhierarchical-attention-networks) - TensorFlow实现的“用于文档分类的层次化注意力网络”\n* [使用CNN进行句子分类](https:\u002F\u002Fgithub.com\u002Fdennybritz\u002Fcnn-text-classification-tf) - TensorFlow实现的“用于句子分类的卷积神经网络”，并配有[博客文章](http:\u002F\u002Fwww.wildml.com\u002F2015\u002F12\u002Fimplementing-a-cnn-for-text-classification-in-tensorflow\u002F)\n* [端到端记忆网络](https:\u002F\u002Fgithub.com\u002Fdomluna\u002Fmemn2n) - “端到端记忆网络”的实现\n* [字符感知神经语言模型](https:\u002F\u002Fgithub.com\u002Fcarpedm20\u002Flstm-char-cnn-tensorflow) - TensorFlow实现的“字符感知神经语言模型”\n* [YOLO TensorFlow ++](https:\u002F\u002Fgithub.com\u002Fthtrieu\u002Fyolotf) - “YOLO：实时目标检测”的TensorFlow实现，支持训练并在移动设备上实现实时运行。\n* [Wavenet](https:\u002F\u002Fgithub.com\u002Fibab\u002Ftensorflow-wavenet) - 这是用于音频生成的[WaveNet生成式神经网络架构]的TensorFlow实现。\n* [记忆下降法](https:\u002F\u002Fgithub.com\u002Ftrigeorgis\u002Fmdm) - TensorFlow实现的“记忆下降法：一种应用于端到端人脸对齐的递归过程”\n* [使用TensorFlow可视化CNN](https:\u002F\u002Fgithub.com\u002FInFoCusp\u002Ftf_cnnvis) - TensorFlow实现的“可视化与理解卷积网络”\n* [VGAN TensorFlow](https:\u002F\u002Fgithub.com\u002FSingularity42\u002FVGAN-Tensorflow) - Vondrick等人关于MIT“生成具有场景动态的视频”的TensorFlow实现\n* [TensorFlow中的3D卷积神经网络](https:\u002F\u002Fgithub.com\u002Fastorfi\u002F3D-convolutional-speaker-recognition) - Torfi等人在TensorFlow中实现的“用于说话人验证应用的3D卷积神经网络”\n* [U-Net](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002Fu-net-brain-tumor) - 用于脑肿瘤分割\n* [空间变换网络](https:\u002F\u002Fgithub.com\u002Fzsdonghao\u002FSpatial-Transformer-Nets) - 学习变换函数\n* [唇读 - 使用TensorFlow中的3D架构进行跨音频-视觉识别](https:\u002F\u002Fgithub.com\u002Fastorfi\u002Flip-reading-deeplearning) - Torfi等人关于“利用深度学习在野外进行跨音频-视觉识别”的TensorFlow实现\n* [注意力对象跟踪](https:\u002F\u002Fgithub.com\u002Fakosiorek\u002Fhart) - “分层注意力循环跟踪”的实现\n* [用于知识图谱补全和链接预测的全息嵌入](https:\u002F\u002Fgithub.com\u002Flaxatives\u002FTensorFlow-TransX) - “知识图谱的全息嵌入”的实现\n* [无监督对象计数](https:\u002F\u002Fgithub.com\u002Fakosiorek\u002Fattend_infer_repeat) - “Attend, Infer, Repeat”的实现\n* [TensorFlow FastText](https:\u002F\u002Fgithub.com\u002Fapcode\u002Ftensorflow_fasttext) - 受Facebook的FastText启发的简单嵌入式文本分类器\n* [MusicGenreClassification](https:\u002F\u002Fgithub.com\u002Fmlachmish\u002FMusicGenreClassification) - 使用神经网络从10秒的音频流中分类音乐类型\n* [Kubeflow](https:\u002F\u002Fgithub.com\u002Fkubeflow\u002Fkubeflow) - 一个便于将TensorFlow与Kubernetes结合使用的框架\n* [TensorNets](https:\u002F\u002Fgithub.com\u002Ftaehoonlee\u002Ftensornets) - 40多种流行的计算机视觉模型，并带有预训练权重。\n* [阶梯网络](https:\u002F\u002Fgithub.com\u002Fdivamgupta\u002Fladder_network_keras) - 在Keras和TensorFlow中实现的用于半监督学习的阶梯网络\n* [TF-Unet](https:\u002F\u002Fgithub.com\u002Fjuniorxsound\u002FTF-Unet) - 在Keras中实现的通用U型网络，用于图像分割\n* [Sarus TF2模型](https:\u002F\u002Fgithub.com\u002Fsarus-tech\u002Ftf2-published-models) - 一系列最近的生成模型，以干净、易于复用的TensorFlow 2代码实现（普通自编码器、VAE、VQ-VAE、PixelCNN、门控PixelCNN、PixelCNN++、PixelSNAIL、条件神经过程）。\n* [Model Maker](https:\u002F\u002Fwww.tensorflow.org\u002Flite\u002Fguide\u002Fmodel_maker) - 一个迁移学习库，简化了TensorFlow Lite模型的训练、评估和部署流程（支持：图像分类、目标检测、文本分类、BERT问答、音频分类、推荐等；[API参考](https:\u002F\u002Fwww.tensorflow.org\u002Flite\u002Fapi_docs\u002Fpython\u002Ftflite_model_maker)）。\n\n\u003Ca name=\"github-powered-by\" \u002F>\n\n\n\n## 由 TensorFlow 提供支持\n\n* [YOLO TensorFlow](https:\u002F\u002Fgithub.com\u002Fgliese581gg\u002FYOLO_tensorflow) - “YOLO：实时目标检测”的实现\n* [android-yolo](https:\u002F\u002Fgithub.com\u002Fnatanielruiz\u002Fandroid-yolo) - 使用 YOLO 网络在 Android 上进行实时目标检测，由 TensorFlow 提供支持。\n* [Magenta](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmagenta) - 一项研究项目，旨在推动音乐和艺术生成领域机器智能的前沿发展\n\n\n\u003Ca name=\"libraries\" \u002F>\n\n## 库\n\n* [TensorFlow Estimators](https:\u002F\u002Fwww.tensorflow.org\u002Fguide\u002Festimators) - 高级 TensorFlow API，极大简化了机器学习编程（最初来自 [tensorflow\u002Fskflow](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fskflow)）\n* [R 语言的 TensorFlow 接口](https:\u002F\u002Ftensorflow.rstudio.com\u002F) - R 语言对 TensorFlow API 的接口，包括 Estimators、Keras、Datasets 等。\n* [Lattice](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Flattice) - 在 TensorFlow 中实现单调校准插值查找表\n* [tensorflow.rb](https:\u002F\u002Fgithub.com\u002Fsomaticio\u002Ftensorflow.rb) - 使用 SWIG 实现的 Ruby 原生 TensorFlow 接口\n* [tflearn](https:\u002F\u002Fgithub.com\u002Ftflearn\u002Ftflearn) - 具有更高层次 API 的深度学习库\n* [TensorLayer](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002Ftensorlayer) - 面向研究人员和工程师的深度学习与强化学习库\n* [TensorFlow-Slim](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmodels\u002Ftree\u002Fmaster\u002Finception\u002Finception\u002Fslim) - 用于定义模型的高级库\n* [TensorFrames](https:\u002F\u002Fgithub.com\u002Ftjhunter\u002Ftensorframes) - Apache Spark 的 TensorFlow 绑定\n* [TensorForce](https:\u002F\u002Fgithub.com\u002Freinforceio\u002Ftensorforce) - TensorForce：一个用于应用强化学习的 TensorFlow 库\n* [TensorFlowOnSpark](https:\u002F\u002Fgithub.com\u002Fyahoo\u002FTensorFlowOnSpark) - 来自 Yahoo! 的倡议，旨在使 TensorFlow 能够与 Apache Spark 配合使用。\n* [caffe-tensorflow](https:\u002F\u002Fgithub.com\u002Fethereon\u002Fcaffe-tensorflow) - 将 Caffe 模型转换为 TensorFlow 格式\n* [keras](http:\u002F\u002Fkeras.io) - 适用于 TensorFlow 和 Theano 的极简、模块化的深度学习库\n* [SyntaxNet：句法的神经网络模型](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmodels\u002Ftree\u002Fmaster\u002Fsyntaxnet) - 对 [Globally Normalized Transition-Based Neural Networks, Andor et al. (2016)](http:\u002F\u002Farxiv.org\u002Fpdf\u002F1603.06042.pdf) 中描述的模型的 TensorFlow 实现\n* [keras-js](https:\u002F\u002Fgithub.com\u002Ftranscranial\u002Fkeras-js) - 在浏览器中运行 Keras 模型（使用 TensorFlow 后端），并支持 GPU 加速\n* [NNFlow](https:\u002F\u002Fgithub.com\u002Fwelschma\u002FNNFlow) - 一个简单的框架，可以将 ROOT NTuple 文件转换为 NumPy 数组，然后在 Google TensorFlow 中使用。\n* [Sonnet](https:\u002F\u002Fgithub.com\u002Fdeepmind\u002Fsonnet) - Sonnet 是 DeepMind 基于 TensorFlow 构建的库，用于构建复杂的神经网络。\n* [tensorpack](https:\u002F\u002Fgithub.com\u002Fppwwyyxx\u002Ftensorpack) - 基于 TensorFlow 的神经网络工具箱，专注于训练速度和大规模数据集。\n* [tf-encrypted](https:\u002F\u002Fgithub.com\u002Fmortendahl\u002Ftf-encrypted) - 在 TensorFlow 之上的一层，用于在加密数据上进行机器学习\n* [pytorch2keras](https:\u002F\u002Fgithub.com\u002Fnerox8664\u002Fpytorch2keras) - 将 PyTorch 模型转换为 Keras 格式（使用 TensorFlow 后端）\n* [gluon2keras](https:\u002F\u002Fgithub.com\u002Fstjordanis\u002Fgluon2keras) - 将 Gluon 模型转换为 Keras 格式（使用 TensorFlow 后端）\n* [TensorIO](https:\u002F\u002Fdoc-ai.github.io\u002Ftensorio\u002F) - 一个轻量级、跨平台的库，用于将 TensorFlow Lite 模型部署到移动设备上。\n* [StellarGraph](https:\u002F\u002Fgithub.com\u002Fstellargraph\u002Fstellargraph) - 图上的机器学习，一个用于图结构（网络结构）数据的 Python 库。\n* [DeepBay](https:\u002F\u002Fgithub.com\u002FElPapi42\u002FDeepBay) - 高级 Keras 补充库，用于实现常见的架构堆栈，作为易于使用的即插即用模块\n* [Tensorflow-Probability](https:\u002F\u002Fwww.tensorflow.org\u002Fprobability) - 基于 TensorFlow 的概率编程，使得在现代硬件上轻松结合概率模型和深度学习成为可能。\n* [TensorLayerX](https:\u002F\u002Fgithub.com\u002Ftensorlayer\u002FTensorLayerX) - TensorLayerX：一个统一的深度学习框架，适用于所有硬件、后端和操作系统，包括 TensorFlow。\n* [Txeo](https:\u002F\u002Fgithub.com\u002Frdabra\u002Ftxeo) - 一个现代化的 C++ 封装，用于 TensorFlow。\n\n\u003Ca name=\"tools-utils\" \u002F>\n\n## 工具\u002F实用程序\n\n* [Speedster](https:\u002F\u002Fgithub.com\u002Fnebuly-ai\u002Fnebullvm\u002Ftree\u002Fmain\u002Fapps\u002Faccelerate\u002Fspeedster) - 自动应用最先进的优化技术，以在您的硬件上实现最大的推理加速。\n* [Guild AI](https:\u002F\u002Fguild.ai) - TensorFlow 的任务运行器和包管理器\n* [ML Workspace](https:\u002F\u002Fgithub.com\u002Fml-tooling\u002Fml-workspace) - 一体化的机器学习和数据科学 Web IDE。它将 TensorFlow、Jupyter、VS Code、TensorBoard 以及其他许多工具\u002F库整合到一个 Docker 镜像中。\n* [create-tf-app](https:\u002F\u002Fgithub.com\u002Fradi-cho\u002Fcreate-tf-app) - 一个用于 TensorFlow 的项目构建命令行工具，涵盖环境管理、代码检查和日志记录。\n\n\u003Ca name=\"video\" \u002F>\n\n## 视频\n\n* [TensorFlow 指南 1](http:\u002F\u002Fbit.ly\u002F1OX8s8Y) - 安装和使用指南\n* [TensorFlow 指南 2](http:\u002F\u002Fbit.ly\u002F1R27Ki9) - 第一集的续集\n* [TensorFlow 基本用法](http:\u002F\u002Fbit.ly\u002F1TCNmEY) - 介绍基本用法的指南\n* [TensorFlow 专家级深度 MNIST](http:\u002F\u002Fbit.ly\u002F1L9IfJx) - 讲解深度 MNIST\n* [TensorFlow Udacity 深度学习](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ReaxoSIM5XQ) - 在 Cloud 9 在线服务上免费安装 TensorFlow 的基本步骤，提供 1GB 数据空间\n* [为什么谷歌希望每个人都能使用 TensorFlow](http:\u002F\u002Fvideo.foxnews.com\u002Fv\u002F4611174773001\u002Fwhy-google-wants-everyone-to-have-access-to-tensorflow\u002F?#sp=show-clips)\n* [2016年1月19日 TensorFlow 硅谷聚会视频](http:\u002F\u002Fblog.altoros.com\u002Fvideos-from-tensorflow-silicon-valley-meetup-january-19-2016.html)\n* [2016年1月21日 TensorFlow 硅谷聚会视频](http:\u002F\u002Fblog.altoros.com\u002Fvideos-from-tensorflow-seattle-meetup-jan-21-2016.html)\n* [斯坦福 CS224d 第7讲 - TensorFlow 介绍，2016年4月19日](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=L8Y2_Cq2X5s&index=7&list=PLmImxx8Char9Ig0ZHSyTqGsdhb9weEGam) - Richard Socher 主讲的 CS224d 自然语言处理深度学习课程\n* [通过 TensorFlow 深入机器学习](https:\u002F\u002Fyoutu.be\u002FGZBIPwdGtkk?list=PLBkISg6QfSX9HL6us70IBs9slFciFFa4W) - 2016 年波特兰俄勒冈州 Pycon 大会，[幻灯片](https:\u002F\u002Fstorage.googleapis.com\u002Famy-jo\u002Ftalks\u002Ftf-workshop.pdf) 和 [代码](https:\u002F\u002Fgithub.com\u002Famygdala\u002Ftensorflow-workshop) 由 Julia Ferraioli、Amy Unruh 和 Eli Bixby 提供\n* [使用 TensorFlow 的大规模深度学习](https:\u002F\u002Fyoutu.be\u002FXYwIDn00PAo) - Jeff Dean 在 2016 年 Spark 峰会上的主题演讲\n* [TensorFlow 和深度学习 - 无需博士学位](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vq2nnJ4g6N0) - 由 Martin Görner 制作\n* [TensorFlow 和深度学习 - 无需博士学位，第二部分（Google Cloud Next '17）](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fTUwdXUFfI8) - 由 Martin Görner 制作\n* [使用 TensorFlow 在 Go 中进行图像识别](https:\u002F\u002Fyoutu.be\u002FP8MZ1Z2LHrw) - 由 Alex Pliutau 制作\n\n\n\n\u003Ca name=\"papers\" \u002F>\n\n## 论文\n\n* [TensorFlow：异构分布式系统上的大规模机器学习](http:\u002F\u002Fdownload.tensorflow.org\u002Fpaper\u002Fwhitepaper2015.pdf) - 本文介绍了 TensorFlow 接口以及我们在 Google 构建的该接口实现\n* [TensorFlow Estimators：高级机器学习框架中的简单性与灵活性管理](https:\u002F\u002Farxiv.org\u002Fpdf\u002F1708.02637.pdf)\n* [TF.Learn：TensorFlow 的分布式机器学习高级模块](https:\u002F\u002Farxiv.org\u002Fabs\u002F1612.04251)\n* [深度学习软件框架比较研究](http:\u002F\u002Farxiv.org\u002Fabs\u002F1511.06435) - 该研究针对多种深度学习架构展开，评估了上述框架在单机环境下分别运行于多线程 CPU 和 GPU（Nvidia Titan X）时的性能\n* [使用 MPI 的分布式 TensorFlow](http:\u002F\u002Farxiv.org\u002Fabs\u002F1603.02339) - 在本文中，我们将最近提出的 Google TensorFlow 扩展到大规模集群上运行，并采用消息传递接口（MPI）\n* [全局归一化的基于转换的神经网络](http:\u002F\u002Farxiv.org\u002Fabs\u002F1603.06042) - 本文描述了 [SyntaxNet](https:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmodels\u002Ftree\u002Fmaster\u002Fsyntaxnet) 背后的模型。\n* [TensorFlow：一个用于大规模机器学习的系统](https:\u002F\u002Farxiv.org\u002Fabs\u002F1605.08695) - 本文将 TensorFlow 数据流模型与其他现有系统进行对比，并展示了其卓越的性能\n* [TensorLayer：高效深度学习开发的多功能库](https:\u002F\u002Farxiv.org\u002Fabs\u002F1707.08551) - 本文介绍了一个多功能 Python 库，旨在帮助研究人员和工程师高效地开发深度学习系统。（荣获 ACM MM 2017 最佳开源软件奖）\n\n\u003Ca name=\"blogs\" \u002F>\n\n## 官方公告\n\n* [TensorFlow：更智能的机器学习，面向所有人](https:\u002F\u002Fgoogleblog.blogspot.com\u002F2015\u002F11\u002Ftensorflow-smarter-machine-learning-for.html) - TensorFlow 的简介\n* [宣布 SyntaxNet：世界上最精确的语法分析器开源](http:\u002F\u002Fgoogleresearch.blogspot.com\u002F2016\u002F05\u002Fannouncing-syntaxnet-worlds-most.html) - 发布 SyntaxNet，“一个基于 TensorFlow 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## 博客文章\n* [官方 TensorFlow 博客](http:\u002F\u002Fblog.tensorflow.org\u002F)\n* [为什么 TensorFlow 将改变人工智能的游戏规则](https:\u002F\u002Farchive.fo\u002Fo9asj)\n* [TensorFlow 对诗人而言](http:\u002F\u002Fpetewarden.com\u002F2016\u002F02\u002F28\u002Ftensorflow-for-poets) - 讲解 TensorFlow 的实现\n* [Scikit Flow 简介 - TensorFlow 的简化接口](http:\u002F\u002Fterrytangyuan.github.io\u002F2016\u002F03\u002F14\u002Fscikit-flow-intro\u002F) - 关键特性详解\n* [在 TensorFlow 中构建机器学习估算器](http:\u002F\u002Fterrytangyuan.github.io\u002F2016\u002F07\u002F08\u002Funderstand-and-build-tensorflow-estimator\u002F) - 深入了解 TensorFlow Learn 估算器的内部机制\n* [TensorFlow - 不仅适用于深度学习](http:\u002F\u002Fterrytangyuan.github.io\u002F2016\u002F08\u002F06\u002Ftensorflow-not-just-deep-learning\u002F)\n* [indico 机器学习团队对 TensorFlow 的看法](https:\u002F\u002Findico.io\u002Fblog\u002Findico-tensorflow)\n* [TensorFlow 的优点、缺点与不足](https:\u002F\u002Findico.io\u002Fblog\u002Fthe-good-bad-ugly-of-tensorflow\u002F) - 对六个月快速发展的回顾（包含技巧、黑客手段及修复问题的代码），Dan Kuster 于 indico 公司，2016年5月9日\n* [TensorFlow 中的 Fizz Buzz](http:\u002F\u002Fjoelgrus.com\u002F2016\u002F05\u002F23\u002Ffizz-buzz-in-tensorflow\u002F) - Joel Grus 的一个玩笑\n* [TensorFlow 中的 RNN：实用指南与未文档化功能](http:\u002F\u002Fwww.wildml.com\u002F2016\u002F08\u002Frnns-in-tensorflow-a-practical-guide-and-undocumented-features\u002F) - 分步指南，附完整代码示例，可在 GitHub 上找到\n* [使用 TensorBoard 可视化 TensorFlow 中的图像分类再训练](http:\u002F\u002Fmaxmelnick.com\u002F2016\u002F07\u002F04\u002Fvisualizing-tensorflow-retrain.html)\n* [TFRecords 指南](http:\u002F\u002Fwarmspringwinds.github.io\u002Ftensorflow\u002Ftf-slim\u002F2016\u002F12\u002F21\u002Ftfrecords-guide\u002F) - 语义分割及 TFRecord 文件格式的处理\n* [TensorFlow Android 指南](https:\u002F\u002Fblog.mindorks.com\u002Fandroid-tensorflow-machine-learning-example-ff0e9b2654cc) - Android TensorFlow 机器学习示例。\n* [现代 Intel® 架构上的 TensorFlow 优化](https:\u002F\u002Fsoftware.intel.com\u002Fen-us\u002Farticles\u002Ftensorflow-optimizations-on-modern-intel-architecture) - 基于 Intel\u002FGoogle 合作，介绍了在 Intel® Xeon® 和 Intel® Xeon Phi™ 处理器平台上对 TensorFlow 的优化\n* [可口可乐的图像识别应用](https:\u002F\u002Fdevelopers.googleblog.com\u002F2017\u002F09\u002Fhow-machine-learning-with-tensorflow.html) - 可口可乐的产品编码图像识别神经网络，带有用户反馈循环\n* [TensorFlow 是如何工作的](https:\u002F\u002Fwww.letslearnai.com\u002F2018\u002F02\u002F02\u002Fhow-does-the-machine-learning-library-tensorflow-work.html) 机器学习库 TensorFlow 是如何工作的？\n\n\n\u003Ca name=\"community\" \u002F>\n\n## 社区\n\n* [Stack Overflow](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Ftensorflow)\n* [@TensorFlow 在 Twitter 上](https:\u002F\u002Ftwitter.com\u002Ftensorflow)\n* [Reddit](https:\u002F\u002Fwww.reddit.com\u002Fr\u002Ftensorflow)\n* [邮件列表](https:\u002F\u002Fgroups.google.com\u002Fa\u002Ftensorflow.org\u002Fforum\u002F#!forum\u002Fdiscuss)\n\n\n\u003Ca name=\"books\" \u002F>\n\n## 书籍\n\n* [使用 TensorFlow 的机器学习 第2版]([http:\u002F\u002Ftensorflowbook.com](https:\u002F\u002Fgithub.com\u002Fchrismattmann\u002FMLwithTensorFlow2ed))，作者为[克里斯·A·马特曼博士](http:\u002F\u002Fgithub.com\u002Fchrismattmann\u002F)，他是加州大学洛杉矶分校的首席数据与人工智能官，同时也是[《Tika 实战》](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Ftika-in-action)一书的作者。本书将人工智能和机器学习这一数学密集型主题以易于理解且实用的方式呈现给初学者。已更新至 TensorFlow 2 和该书的最新版本。\n* [与 TensorFlow 的初次接触](http:\u002F\u002Fwww.jorditorres.org\u002Ffirst-contact-with-tensorflow\u002F)，作者为巴塞罗那理工大学教授、巴塞罗那超级计算中心的研究经理兼高级顾问乔迪·托雷斯。\n* [用 Python 进行深度学习](https:\u002F\u002Fmachinelearningmastery.com\u002Fdeep-learning-with-python\u002F)——使用 Keras 在 Theano 和 TensorFlow 上开发深度学习模型，作者为杰森·布朗利。\n* [TensorFlow 用于机器智能](https:\u002F\u002Fbleedingedgepress.com\u002Ftensor-flow-for-machine-intelligence\u002F)——从图计算的基础知识到深度学习模型，再到在生产环境中使用 TensorFlow 的完整指南——Bleeding Edge Press 出版。\n* [TensorFlow 入门](https:\u002F\u002Fwww.packtpub.com\u002Fbig-data-and-business-intelligence\u002Fgetting-started-tensorflow)——由詹卡洛·扎科内编写，帮助读者快速上手谷歌最新的数值计算库，并深入挖掘数据。\n* [使用 Scikit-Learn 和 TensorFlow 的动手机器学习](http:\u002F\u002Fshop.oreilly.com\u002Fproduct\u002F0636920052289.do)——作者为奥雷利安·热隆，他曾任 YouTube 视频分类团队负责人。本书涵盖机器学习基础、利用 TensorFlow 在多台服务器和 GPU 上训练与部署深度网络、最新的 CNN、RNN 和自编码器架构，以及强化学习（Deep Q）等内容。\n* [使用 TensorFlow 构建机器学习项目](https:\u002F\u002Fwww.packtpub.com\u002Fbig-data-and-business-intelligence\u002Fbuilding-machine-learning-projects-tensorflow)——作者为罗道尔福·邦宁。本书介绍了多个 TensorFlow 项目，展示了在不同场景下使用 TensorFlow 可以实现的功能。书中提供了关于模型训练、机器学习、深度学习以及各种神经网络操作的项目。每个项目都具有吸引力且富有洞察力，能够教会读者如何使用 TensorFlow，并展示通过张量操作如何探索数据的层次结构。\n* [使用 TensorLayer 进行深度学习](http:\u002F\u002Fwww.broadview.com.cn\u002Fbook\u002F5059)——由董浩等人编写。本书同时涵盖了深度学习以及使用 TensorFlow 和 TensorLayer 的实现方法。\n* [TensorFlow 2.0 实战](https:\u002F\u002Fwww.manning.com\u002Fbooks\u002Ftensorflow-in-action)——作者为图山·加内格达拉。这本实用的指南介绍了如何利用 TensorFlow 2.0 的新特性构建深度学习模型，内容丰富，语言通俗易懂，并覆盖了最新的算法。\n* [面向黑客的概率编程与贝叶斯方法](https:\u002F\u002Fgithub.com\u002FCamDavidsonPilon\u002FProbabilistic-Programming-and-Bayesian-Methods-for-Hackers)——作者为卡梅伦·戴维森-皮隆。介绍使用 tensorflow-probability（或 PyMC2\u002F3）进行贝叶斯方法和概率图模型的入门知识。\n\n\n\n\u003Ca name=\"contributions\" \u002F>\n\n## 贡献\n\n我们始终欢迎您的贡献！\n\n如果您希望为本列表贡献力量（请务必这样做），可以向我发送 Pull Request，或通过 [@jtoy](https:\u002F\u002Ftwitter.com\u002Fjtoy) 与我联系。\n\n此外，如果您发现上述任何仓库因以下原因之一应被弃用：\n\n* 仓库所有者明确表示“此库不再维护”；\n* 长期未提交代码（2~3 年）。\n\n更多信息请参阅[贡献指南](https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow\u002Fblob\u002Fmaster\u002Fcontributing.md)。\n\n\n\u003Ca name=\"credits\" \u002F>\n\n## 致谢\n\n* 部分 Python 库摘录自 [vinta](https:\u002F\u002Fgithub.com\u002Fvinta\u002Fawesome-python)。\n* 我找到的一些 Go 相关参考资料来自[此页面](https:\u002F\u002Fcode.google.com\u002Fp\u002Fgo-wiki\u002Fwiki\u002FProjects#Machine_Learning)。","# Awesome TensorFlow 快速上手指南\n\n`awesome-tensorflow` 并非一个可直接安装的软件库，而是一个精选的 TensorFlow 实验、库、项目和教程的资源列表。本指南将帮助你快速搭建 TensorFlow 环境，并利用该列表中的资源开始深度学习之旅。\n\n## 环境准备\n\n在开始之前，请确保你的开发环境满足以下要求：\n\n*   **操作系统**: Windows, macOS, 或 Linux (推荐 Ubuntu 18.04+)\n*   **Python**: 版本 3.7 - 3.10 (推荐 3.8 或 3.9)\n*   **硬件**: \n    *   CPU: 任意现代多核处理器\n    *   GPU (可选但推荐): NVIDIA GPU (需安装 CUDA Toolkit 和 cuDNN) 以加速训练\n*   **包管理器**: `pip` (通常随 Python 安装) 或 `conda`\n\n**前置依赖检查**:\n建议在虚拟环境中操作，以避免依赖冲突。\n\n```bash\n# 创建并激活虚拟环境 (使用 venv)\npython -m venv tf-env\nsource tf-env\u002Fbin\u002Factivate  # Linux\u002FmacOS\n# tf-env\\Scripts\\activate   # Windows\n```\n\n## 安装步骤\n\n由于 `awesome-tensorflow` 是资源列表，你需要安装的是 **TensorFlow** 核心库。国内开发者推荐使用清华或阿里镜像源以加速下载。\n\n### 1. 配置国内镜像源 (推荐)\n\n```bash\npip config set global.index-url https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n\n### 2. 安装 TensorFlow\n\n**方案 A: 安装 CPU 版本 (适合初学者或无 NVIDIA GPU 的用户)**\n```bash\npip install tensorflow-cpu\n```\n\n**方案 B: 安装 GPU 版本 (适合有 NVIDIA GPU 的用户)**\n*注意：请根据你的显卡驱动版本确认所需的 CUDA 和 cuDNN 版本，参考 [TensorFlow 官方兼容性表](https:\u002F\u002Fwww.tensorflow.org\u002Finstall\u002Fsource#gpu)。*\n\n```bash\npip install tensorflow\n```\n\n### 3. 验证安装\n\n运行以下命令检查是否安装成功：\n\n```bash\npython -c \"import tensorflow as tf; print(tf.__version__); print('GPU Available:', tf.config.list_physical_devices('GPU'))\"\n```\n\n## 基本使用\n\n安装完成后，你可以参考 `awesome-tensorflow` 列表中的 [Tutorials](https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow#tutorials) 部分进行学习。以下是一个基于 TensorFlow 2.x 的最简示例，展示如何构建并训练一个简单的神经网络。\n\n### 最简代码示例 (Hello World)\n\n此示例加载 MNIST 手写数字数据集，构建模型并进行训练。\n\n```python\nimport tensorflow as tf\n\n# 1. 加载数据 (MNIST 手写数字)\nmnist = tf.keras.datasets.mnist\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\n\n# 2. 数据预处理 (归一化)\nx_train, x_test = x_train \u002F 255.0, x_test \u002F 255.0\n\n# 3. 构建模型\nmodel = tf.keras.models.Sequential([\n  tf.keras.layers.Flatten(input_shape=(28, 28)),\n  tf.keras.layers.Dense(128, activation='relu'),\n  tf.keras.layers.Dropout(0.2),\n  tf.keras.layers.Dense(10)\n])\n\n# 4. 编译模型\nmodel.compile(optimizer='adam',\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])\n\n# 5. 训练模型\nprint(\"开始训练...\")\nmodel.fit(x_train, y_train, epochs=5)\n\n# 6. 评估模型\ntest_loss, test_acc = model.evaluate(x_test,  y_test, verbose=2)\nprint(f'\\n测试集准确率：{test_acc:.4f}')\n\n# 7. 进行预测\nprobability_model = tf.keras.Sequential([model, tf.keras.layers.Softmax()])\npredictions = probability_model.predict(x_test[:1])\nprint(f\"第一张图的预测概率分布：{predictions[0]}\")\n```\n\n### 进阶资源指引\n\n完成基础练习后，请访问 `awesome-tensorflow` 仓库探索更多专项内容：\n\n*   **项目模板**: 参考 `Models\u002FProjects` 中的 `Tensorflow-Project-Template` 规范你的代码结构。\n*   **经典模型复现**: 查找 `Neural Style`, `GAN`, `BERT` 等具体实现代码。\n*   **中文教程**: 特别关注列表中标记为 `[CN]` 的 `TensorLayer` 文档或其他中文社区贡献的教程链接。","某初创公司的算法工程师小李，正负责为一款智能穿戴设备开发基于手机传感器数据的人体活动识别模型，急需快速构建并优化 TensorFlow 深度学习方案。\n\n### 没有 awesome-tensorflow 时\n- **资源分散难筛选**：需要在 GitHub、博客和论坛中大海捞针，难以辨别哪些教程适合初学者或特定硬件（如树莓派）。\n- **复现成本高昂**：缺乏经过验证的 LSTM 时间序列分类代码示例，导致从头编写数据预处理和网络结构耗时数周。\n- **部署路径模糊**：不清楚如何将训练好的模型迁移到 Android 端或嵌入式设备，常因环境配置错误而停滞不前。\n- **最佳实践缺失**：容易陷入过时的 API 用法，不了解 Estimator 等高效接口，导致代码维护困难且运行效率低。\n\n### 使用 awesome-tensorflow 后\n- **一站式精准导航**：直接通过分类目录找到\"Classification on time series\"和\"Installing TensorFlow on Raspberry Pi\"等针对性极强的项目链接。\n- **代码快速落地**：复用仓库中成熟的 Jupyter Notebook 教程和 seq2seq 预测示例，将原型开发周期从数周缩短至几天。\n- **端到端部署清晰**：依据整理的 Android 应用构建指南和边缘计算案例，顺利打通从模型训练到移动端部署的全流程。\n- **紧跟技术前沿**：参考\"Effective Tensorflow\"中的最佳实践和斯坦福课程资料，确保架构设计符合当前主流标准，提升模型性能。\n\nawesome-tensorflow 通过将散落的优质资源系统化整理，极大地降低了深度学习项目的入门门槛与试错成本，让开发者能专注于核心业务逻辑的创新。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fjtoy_awesome-tensorflow_db569965.png","jtoy",null,"https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fjtoy_1d8e88f6.jpg","researching general intelligence. currently focused on grid cells and compositionality.","http:\u002F\u002Fblog.jtoy.net","https:\u002F\u002Fgithub.com\u002Fjtoy",17614,2992,"2026-04-18T18:06:26","CC0-1.0",1,"","未说明",{"notes":86,"python":84,"dependencies":87},"awesome-tensorflow 是一个 curated list（精选列表），本身不是可运行的软件工具，而是收集了各种 TensorFlow 教程、项目、库和资源的链接集合。因此它没有具体的运行环境需求。用户需要根据列表中具体引用的各个项目或教程的 README 来确定相应的环境配置。通常这些项目基于 TensorFlow 框架，可能需要 Python、GPU 支持以及特定的依赖库，但具体要求因项目而异。",[88],"TensorFlow",[14],"2026-03-27T02:49:30.150509","2026-04-19T15:38:20.200734",[93,98,103,108,113,118,123],{"id":94,"question_zh":95,"answer_zh":96,"source_url":97},42359,"如何向该列表添加新的书籍、项目或资源？","维护者建议通过提交 Pull Request (PR) 的方式来添加新内容。请在 Issue 中说明想添加的内容，然后创建一个 PR 将其合并到项目中。","https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow\u002Fissues\u002F198",{"id":99,"question_zh":100,"answer_zh":101,"source_url":102},42360,"收录的资源必须严格限定于 TensorFlow 吗？","是的，该列表主要收录 TensorFlow 特定的资源。例如，《Deep Learning》这本由 Ian Goodfellow 等人撰写的经典书籍虽然很棒，但因为不是专门针对 TensorFlow 的，所以未被收录。","https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow\u002Fissues\u002F75",{"id":104,"question_zh":105,"answer_zh":106,"source_url":107},42361,"发现列表中的链接失效了怎么办？","可以直接在 Issue 中报告失效的链接（包括具体哪个标题下的链接），维护者通常会尽快修复并更新为正确的源地址。","https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow\u002Fissues\u002F126",{"id":109,"question_zh":110,"answer_zh":111,"source_url":112},42362,"有推荐的 TensorFlow 开发集成开发环境（IDE）或工具吗？","关于 IDE 的选择，该项目维护者个人推荐使用 Vim 进行 TensorFlow 开发。用户也可以提议在列表中添加专门的 IDE 和工具选择章节。","https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow\u002Fissues\u002F20",{"id":114,"question_zh":115,"answer_zh":116,"source_url":117},42363,"是否有预训练的 TensorFlow 模型或权重可供使用？","有的，社区鼓励贡献类似 Caffe Model Zoo 的 TensorFlow 模型库。例如，已有人贡献了转换后的 VGG Face 权重，这类资源会被添加到列表的模型或项目部分。","https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow\u002Fissues\u002F23",{"id":119,"question_zh":120,"answer_zh":121,"source_url":122},42364,"如何确认列表中的项目是否真的基于 TensorFlow 编写？","如果发现某个被列为 TensorFlow 的项目实际上是用其他框架（如 Torch7）编写的，可以提交 Issue 指出错误。维护者核实后会立即修正或删除该项目条目。","https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow\u002Fissues\u002F11",{"id":124,"question_zh":125,"answer_zh":126,"source_url":127},42365,"在哪里可以找到相关的斯坦福大学 TensorFlow 课程资源？","可以关注斯坦福大学的 CS20SI 课程（TensorFlow for DeepLearning Research）。该课程提供了教学大纲、幻灯片、笔记和作业代码（包含风格迁移等示例），虽然没有视频，但代码和笔记非常有价值。","https:\u002F\u002Fgithub.com\u002Fjtoy\u002Fawesome-tensorflow\u002Fissues\u002F102",[]]