[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-instillai--TensorFlow-Course":3,"tool-instillai--TensorFlow-Course":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",159636,2,"2026-04-17T23:33:34",[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。",108322,"2026-04-10T11:39:34",[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":76,"owner_location":76,"owner_email":77,"owner_twitter":76,"owner_website":78,"owner_url":79,"languages":80,"stars":92,"forks":93,"last_commit_at":94,"license":95,"difficulty_score":32,"env_os":96,"env_gpu":96,"env_ram":96,"env_deps":97,"category_tags":102,"github_topics":103,"view_count":32,"oss_zip_url":76,"oss_zip_packed_at":76,"status":17,"created_at":108,"updated_at":109,"faqs":110,"releases":141},9023,"instillai\u002FTensorFlow-Course","TensorFlow-Course",":satellite: Simple and ready-to-use tutorials for TensorFlow ","TensorFlow-Course 是一个专为 TensorFlow 打造的开源教程仓库，旨在提供简单易懂、即拿即用的学习资源。针对深度学习框架虽然强大但入门门槛较高、官方文档对新手不够友好的痛点，该项目通过结构化的实战案例，帮助学习者快速跨越从理论到代码的鸿沟。\n\n每个教程都配备了完整的源代码和详细的配套文档，内容已更新至 TensorFlow 2.3 版本，并涵盖了如何利用 Keras 等高层 API 简化模型构建的技巧。这种“代码 + 文档”双轨并行的模式，有效解决了初学者在面对高度模块化设计时容易产生的困惑，让复杂的神经网络搭建过程变得清晰直观。\n\n无论是刚刚接触人工智能的学生、希望快速上手的开发者，还是从事相关领域的研究人员，都能从中获益。对于想要系统掌握 TensorFlow 核心概念并付诸实践的用户来说，TensorFlow-Course 提供了一条清晰的学习路径，帮助大家更高效地开启深度学习之旅。","\n\n********************\n`TensorFlow Course`_\n********************\n.. image:: https:\u002F\u002Ftravis-ci.org\u002Finstillai\u002FTensorFlow-Course.svg?branch=master\n    :target: https:\u002F\u002Ftravis-ci.org\u002Finstillai\u002FTensorFlow-Course\n.. image:: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fcontributions-welcome-brightgreen.svg?style=flat\n    :target: https:\u002F\u002Fgithub.com\u002Fopen-source-for-science\u002FTensorFlow-Course\u002Fpulls\n.. image:: https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fmachinemindset.svg?label=Follow&style=social\n    :target: https:\u002F\u002Ftwitter.com\u002Fmachinemindset\n.. image:: https:\u002F\u002Fzenodo.org\u002Fbadge\u002F151300862.svg\n   :target: https:\u002F\u002Fzenodo.org\u002Fbadge\u002Flatestdoi\u002F151300862\n\n\nThis repository aims to provide simple and ready-to-use tutorials for TensorFlow.\nEach tutorial includes ``source code`` and most of them are associated with a ``documentation``.\n\n.. .. image:: _img\u002Fmainpage\u002FTensorFlow_World.gif\n\n.. The links.\n.. _TensorFlow: https:\u002F\u002Fwww.tensorflow.org\u002Finstall\u002F\n.. _Wikipedia: https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTensorFlow\u002F\n\n\n##########################################################################\nSponsorship\n##########################################################################\n\nTo support maintaining and upgrading this project, please kindly consider `Sponsoring the project developer \u003Chttps:\u002F\u002Fgithub.com\u002Fsponsors\u002Fastorfi\u002Fdashboard>`_.\n\nAny level of support is a great contribution here :heart:\n\n**Status:** *This project has been updated to **TensorFlow 2.3**.*\n\n\n#################\nTable of Contents\n#################\n.. contents::\n  :local:\n  :depth: 3\n\n\n==========================================\nDownload Free TensorFlow Roadmap EBook\n==========================================\n\n.. raw:: html\n\n   \u003Cdiv align=\"center\">\n\n.. raw:: html\n\n \u003Ca href=\"http:\u002F\u002Fwww.machinelearningmindset.com\u002Ftensorflow-roadmap-ebook\u002F\" target=\"_blank\">\n  \u003Cimg width=\"710\" height=\"500\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_1bb1678f6fbd.png\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n==========================================\nSlack Group\n==========================================\n\n.. raw:: html\n\n   \u003Cdiv align=\"center\">\n\n.. raw:: html\n\n \u003Ca href=\"https:\u002F\u002Fwww.machinelearningmindset.com\u002Fslack-group\u002F\" target=\"_blank\">\n  \u003Cimg width=\"1033\" height=\"350\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_2f07fac72f26.png\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n\n\n~~~~~~~~~~~~~~~~~~~~~\nWhat is TensorFlow?\n~~~~~~~~~~~~~~~~~~~~~\nTensorFlow is an open-source software library for dataflow programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks. It is used for both research and production at Google often replacing its closed-source predecessor, DistBelief.\n\nTensorFlow was developed by the Google Brain team for internal Google use. It was released under the Apache 2.0 open source license on November 9, 2015.\n\n\n============\nMotivation\n============\n\nThere are different motivations for this open source project. TensorFlow (as we write this document) is one of \u002F the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web?\n\n~~~~~~~~~~~~~~~~~~~~~\nWhy use TensorFlow?\n~~~~~~~~~~~~~~~~~~~~~\n\nDeep Learning is in very high interest these days - there's a crucial need for rapid and optimized implementations of the algorithms and architectures. TensorFlow is designed to facilitate this goal.\n\nThe strong advantage of TensorFlow is it flexibility in designing highly modular models which can also be a disadvantage for beginners since a lot of the pieces must be considered together when creating the model.\n\nThis issue has been facilitated as well by developing high-level APIs such as `Keras \u003Chttps:\u002F\u002Fkeras.io\u002F>`_ and `Slim \u003Chttps:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmodels\u002Fblob\u002F031a5a4ab41170d555bc3e8f8545cf9c8e3f1b28\u002Fresearch\u002Finception\u002Finception\u002Fslim\u002FREADME.md>`_ which abstract a lot of the pieces used in designing machine learning algorithms.\n\nThe interesting thing about TensorFlow is that **it can be found anywhere these days**. Lots of the researchers and developers are using it and *its community is growing at the speed of light*! So many issues can be dealt with easily since they're usually the same issues that a lot of other people run into considering the large number of people involved in the TensorFlow community.\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nWhat's the point of this repository?\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n**Developing open source projects for the sake of just developing something is not the reason behind this effort**.\nConsidering the large number of tutorials that are being added to this large community, this repository has been created to break the jump-in and jump-out process that usually happens to most of the open source projects, **but why and how**?\n\nFirst of all, what's the point of putting effort into something that most of the people won't stop by and take a look? What's the point of creating something that does not help anyone in the developers and researchers community? Why spend time for something that can easily be forgotten? But **how we try to do it?** Even up to this\nvery moment there are countless tutorials on TensorFlow whether on the model design or TensorFlow\nworkflow.\n\nMost of them are too complicated or suffer from a lack of documentation. There are only a few available tutorials which are concise and well-structured and provide enough insight for their specific implemented models.\n\nThe goal of this project is to help the community with structured tutorials and simple and optimized code implementations to provide better insight about how to use TensorFlow *quick and effectively*.\n\nIt is worth noting that, **the main goal of this project is to provide well-documented tutorials and less-complicated code**!\n\n=================================================\nTensorFlow Installation and Setup the Environment\n=================================================\n\n\n.. image:: _img\u002Fmainpage\u002Finstallation-logo.gif\n   :height: 100px\n   :width: 200 px\n   :scale: 50 %\n   :alt: alternate text\n   :align: right\n   :target: docs\u002Ftutorials\u002Finstallation\n\n.. _TensorFlow Installation: https:\u002F\u002Fwww.tensorflow.org\u002Finstall\n\nIn order to install TensorFlow please refer to the following link:\n\n  * `TensorFlow Installation`_\n\n\n.. image:: _img\u002Fmainpage\u002Finstallation.gif\n    :target: https:\u002F\u002Fwww.tensorflow.org\u002Finstall\n\nThe virtual environment installation is recommended in order to prevent package conflict and having the capacity to customize the working environment.\n\n====================\nTensorFlow Tutorials\n====================\n\nThe tutorials in this repository are partitioned into relevant categories.\n\n==========================\n\n~~~~~~~~\nWarm-up\n~~~~~~~~\n\n.. image:: _img\u002Fmainpage\u002Fwelcome.gif\n   :height: 100px\n   :width: 200 px\n   :scale: 50 %\n   :alt: alternate text\n   :align: right\n\n\n.. _colab: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F0-welcome\u002Fwelcome.ipynb\n.. _Documentationcnnwelcome: docs\u002Ftutorials\u002F0-welcome\n.. _ipythonwelcome: codes\u002Fipython\u002F0-welcome\u002Fwelcome.ipynb\n.. _pythonwelcome: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002F0-welcome\u002Fwelcome.py\n.. _videowelcome: https:\u002F\u002Fyoutu.be\u002Fxd0DVygHlNE\n\n\n.. |Welcome| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n   :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F0-welcome\u002Fwelcome.ipynb\n\n.. |youtubeim| image:: _img\u002Fmainpage\u002FYouTube.png\n  :target: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002F_img\u002Fmainpage\u002FYouTube.png\n\n\n+----+---------------------+--------------------------+------------------------------------------------------------------------+-------------------------------------------+\n| #  |       topic         |          Run             |  Source Code                                                           |  Media                                    |\n+====+=====================+==========================+========================================================================+===========================================+\n| 1  | Start-up            |       |Welcome|          | `Notebook \u003Cipythonwelcome_>`_  \u002F `Python \u003Cpythonwelcome_>`_            | `Video Tutorial \u003Cvideowelcome_>`_         |\n+----+---------------------+--------------------------+------------------------------------------------------------------------+-------------------------------------------+\n\n==========================\n\n~~~~~~\nBasics\n~~~~~~\n\n.. raw:: html\n\n   \u003Cdiv align=\"left\">\n\n.. raw:: html\n\n \u003Ca href=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_335b3d0951a1.gif\" target=\"_blank\">\n  \u003Cimg width=\"250\" height=\"250\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_335b3d0951a1.gif\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n.. raw:: html\n\n   \u003Cbr>\n\n\n\n.. _ipythontensors: codes\u002Fipython\u002F1-basics\u002Ftensors.ipynb\n.. _pythontensors: codes\u002Fpython\u002F1-basics\u002Ftensors.py\n.. _videotensors: https:\u002F\u002Fyoutu.be\u002FOd-VvnYUbFw\n.. |Tensors| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F1-basics\u002Ftensors.ipynb\n\n.. _ipythonad: codes\u002Fipython\u002F1-basics\u002Fautomatic_differentiation.ipynb\n.. _pythonad: codes\u002Fpython\u002F1-basics\u002Fautomatic_differentiation.py\n.. _videoad: https:\u002F\u002Fyoutu.be\u002Fl-MGydWW-UE\n.. |AD| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F1-basics\u002Fautomatic_differentiation.ipynb\n\n.. _ipythongraphs: codes\u002Fipython\u002F1-basics\u002Fgraph.ipynb\n.. _pythongraphs: codes\u002Fpython\u002F1-basics\u002Fgraph.py\n.. _videographs: https:\u002F\u002Fyoutu.be\u002FP9xA1s6AUNk\n.. |graphs| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F1-basics\u002Fgraph.ipynb\n\n\n.. _ipythonmodels: codes\u002Fipython\u002F1-basics\u002Fmodels.ipynb\n.. _pythonmodels: codes\u002Fpython\u002F1-basics\u002Fmodels.py\n.. _videomodels: https:\u002F\u002Fyoutu.be\u002FWnlUE04REOY\n.. |models| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F1-basics\u002Fmodels.ipynb\n\n\n\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------+-----------------------------------------+\n| #  |       topic                       |          Run             |  Source Code                                                           |        Media                            |\n+====+===================================+==========================+========================================================================+=========================================+\n| 1  | Tensors                           |       |Tensors|          | `Notebook \u003Cipythontensors_>`_  \u002F `Python \u003Cpythontensors_>`_            | `Video Tutorial \u003Cvideotensors_>`_       |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------+-----------------------------------------+\n| 2  | Automatic Differentiation         |       |AD|               | `Notebook \u003Cipythonad_>`_  \u002F `Python \u003Cpythonad_>`_                      | `Video Tutorial \u003Cvideoad_>`_            |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------+-----------------------------------------+\n| 3  | Introduction to Graphs            |       |graphs|           | `Notebook \u003Cipythongraphs_>`_ \u002F `Python \u003Cpythongraphs_>`_               | `Video Tutorial \u003Cvideographs_>`_        |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------+-----------------------------------------+\n| 4  | TensorFlow Models                 |       |models|           | `Notebook \u003Cipythonmodels_>`_  \u002F `Python \u003Cpythonmodels_>`_              | `Video Tutorial \u003Cvideomodels_>`_        |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------+-----------------------------------------+\n\n==========================\n\n~~~~~~~~~~~~~~~~~~~~~~\nBasic Machine Learning\n~~~~~~~~~~~~~~~~~~~~~~\n\n.. raw:: html\n\n   \u003Cdiv align=\"left\">\n\n.. raw:: html\n\n \u003Ca href=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_75e92653de4e.gif\" target=\"_blank\">\n  \u003Cimg width=\"250\" height=\"250\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_75e92653de4e.gif\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n.. raw:: html\n\n   \u003Cbr>\n\n.. .. image:: _img\u002Fmainpage\u002Fbasicmodels.gif\n..    :height: 100px\n..    :width: 200 px\n..    :scale: 50 %\n..    :alt: alternate text\n..    :align: right\n\n\n.. _ipythonlinearreg: codes\u002Fipython\u002Fbasics_in_machine_learning\u002Flinearregression.ipynb\n.. _pythonlinearreg: codes\u002Fpython\u002Fbasics_in_machine_learning\u002Flinearregression.py\n.. _tutoriallinearreg: https:\u002F\u002Fwww.machinelearningmindset.com\u002Flinear-regression-with-tensorflow\u002F\n.. _videoinearreg: https:\u002F\u002Fyoutu.be\u002F2RTBBiKKuLI\n\n.. _tutorialdataaugmentation: https:\u002F\u002Fwww.machinelearningmindset.com\u002Fdata-augmentation-with-tensorflow\u002F\n.. _ipythondataaugmentation: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fbasics_in_machine_learning\u002Fdataaugmentation.ipynb\n.. _pythondataaugmentation: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fbasics_in_machine_learning\u002Fdataaugmentation.py\n.. _videodataaugmentation: https:\u002F\u002Fyoutu.be\u002FHbzR2snHJF0\n\n.. |lr| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fbasics_in_machine_learning\u002Flinearregression.ipynb\n.. |da| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fbasics_in_machine_learning\u002Fdataaugmentation.ipynb\n\n\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------------------+----------------------------------------------+----------------------------------------------+\n| #  |       topic                       |          Run             |  Source Code                                                                       |  More                                        |           Media                              |\n+====+===================================+==========================+====================================================================================+==============================================+==============================================+\n| 1  | Linear Regression                 |       |lr|               | `Notebook \u003Cipythonlinearreg_>`_  \u002F `Python \u003Cpythonlinearreg_>`_                    | `Tutorial \u003Ctutoriallinearreg_>`_             | `Video Tutorial \u003Cvideoinearreg_>`_           |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------------------+----------------------------------------------+----------------------------------------------+\n| 2  | Data Augmentation                 |       |da|               | `Notebook \u003Cipythondataaugmentation_>`_ \u002F `Python \u003Cpythondataaugmentation_>`_       | `Tutorial \u003Ctutorialdataaugmentation_>`_      | `Video Tutorial \u003Cvideodataaugmentation_>`_   |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------------------+----------------------------------------------+----------------------------------------------+\n\n\n\n.. +----+----------------------------+----------------------------------------------------------------------------------------+----------------------------------------------+\n\n==========================\n\n~~~~~~~~~~~~~~~~\nNeural Networks\n~~~~~~~~~~~~~~~~\n\n.. raw:: html\n\n   \u003Cdiv align=\"left\">\n\n.. raw:: html\n\n \u003Ca href=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_4159e99af90c.png\" target=\"_blank\">\n  \u003Cimg width=\"600\" height=\"180\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_4159e99af90c.png\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n.. raw:: html\n\n    \u003Cbr>\n\n\n.. _ipythonmlp: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fneural_networks\u002Fmlp.ipynb\n.. _pythonmlp: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fneural_networks\u002Fmlp.py\n.. _videomlp: https:\u002F\u002Fyoutu.be\u002Fw20efZqSK2Y\n\n.. _ipythoncnn: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fneural_networks\u002FCNNs.ipynb\n.. _pythoncnn: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fneural_networks\u002Fcnns.py\n.. _videocnn: https:\u002F\u002Fyoutu.be\u002FWVifZBCRz8g\n\n\n.. |mlp| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fneural_networks\u002Fmlp.ipynb\n.. |cnn| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fneural_networks\u002FCNNs.ipynb\n\n\n+----+------------------------------------------+--------------------------+------------------------------------------------------+------------------------------------+\n| #  |       topic                              |          Run             |  Source Code                                         |            Media                   |\n+====+==========================================+==========================+======================================================+====================================+\n| 1  |  *Multi Layer Perceptron*                |       |mlp|              | `Notebook \u003Cipythonmlp_>`_ \u002F `Python \u003Cpythonmlp_>`_   | `Video Tutorial \u003Cvideomlp_>`_      |\n+----+------------------------------------------+--------------------------+------------------------------------------------------+------------------------------------+\n| 2  |  *Convolutional Neural Networks*         |       |cnn|              | `Notebook \u003Cipythoncnn_>`_ \u002F `Python \u003Cpythoncnn_>`_   | `Video Tutorial \u003Cvideocnn_>`_      |\n+----+------------------------------------------+--------------------------+------------------------------------------------------+------------------------------------+\n\n==========================\n\n~~~~~~~~~~~~~~~~\nAdvanced\n~~~~~~~~~~~~~~~~\n\n\n.. raw:: html\n\n   \u003Cdiv align=\"left\">\n\n.. raw:: html\n\n \u003Ca href=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_59783e4cb798.png\" target=\"_blank\">\n  \u003Cimg width=\"180\" height=\"180\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_59783e4cb798.png\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n.. raw:: html\n\n    \u003Cbr>\n\n\n\n\n.. _ipythoncustomtr: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Fcustom_training.ipynb\n.. _pythoncustomtr: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fadvanced\u002Fcustom_training.py\n.. _videocustomtr: https:\u002F\u002Fyoutu.be\u002Fz5gcabfyPfA\n\n.. _ipythondgenerator: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Fdataset_generator.ipynb\n.. _pythondgenerator: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fadvanced\u002Fdataset_generator.py\n.. _videodgenerator: https:\u002F\u002Fyoutu.be\u002F-YsgMdDPu3g\n\n.. _ipythontfrecords: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Ftfrecords.ipynb\n.. _pythontfrecords: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fadvanced\u002Ftfrecords.py\n.. _videotfrecords: https:\u002F\u002Fyoutu.be\u002Fzqavy_5QMk8\n\n\n.. |ctraining| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Fcustom_training.ipynb\n\n.. |dgenerator| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Fdataset_generator.ipynb\n\n.. |tfrecords| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Ftfrecords.ipynb\n\n\n+----+------------------------------------------+--------------------------+--------------------------------------------------------------------+----------------------------------------+\n| #  |       topic                              |          Run             |  Source Code                                                       |           Media                        |\n+====+==========================================+==========================+====================================================================+========================================+\n| 1  |  *Custom Training*                       |       |ctraining|        | `Notebook \u003Cipythoncustomtr_>`_ \u002F `Python \u003Cpythoncustomtr_>`_       | `Video Tutorial \u003Cvideocustomtr_>`_     |\n+----+------------------------------------------+--------------------------+--------------------------------------------------------------------+----------------------------------------+\n| 2  |  *Dataset Generator*                     |       |dgenerator|       | `Notebook \u003Cipythondgenerator_>`_ \u002F `Python \u003Cpythondgenerator_>`_   | `Video Tutorial \u003Cvideodgenerator_>`_   |\n+----+------------------------------------------+--------------------------+--------------------------------------------------------------------+----------------------------------------+\n| 3  |  *Create TFRecords*                      |       |tfrecords|        | `Notebook \u003Cipythontfrecords_>`_ \u002F `Python \u003Cpythontfrecords_>`_     | `Video Tutorial \u003Cvideotfrecords_>`_    |\n+----+------------------------------------------+--------------------------+--------------------------------------------------------------------+----------------------------------------+\n\n\n\n=====================\nSome Useful Tutorials\n=====================\n\n  * `TensorFlow Examples \u003Chttps:\u002F\u002Fgithub.com\u002Faymericdamien\u002FTensorFlow-Examples>`_ - TensorFlow tutorials and code examples for beginners\n  * `Sungjoon's TensorFlow-101 \u003Chttps:\u002F\u002Fgithub.com\u002Fsjchoi86\u002FTensorflow-101>`_ - TensorFlow tutorials written in Python with Jupyter Notebook\n  * `Terry Um’s TensorFlow Exercises \u003Chttps:\u002F\u002Fgithub.com\u002Fterryum\u002FTensorFlow_Exercises>`_ - Re-create the codes from other TensorFlow examples\n  * `Classification on time series \u003Chttps:\u002F\u002Fgithub.com\u002Fguillaume-chevalier\u002FLSTM-Human-Activity-Recognition>`_ - Recurrent Neural Network classification in TensorFlow with LSTM on cellphone sensor data\n\n\n=============\nContributing\n=============\n\nWhen contributing to this repository, please first discuss the change you wish to make via issue,\nemail, or any other method with the owners of this repository before making a change. *For typos, please\ndo not create a pull request. Instead, declare them in issues or email the repository owner*.\n\nPlease note we have a code of conduct, please follow it in all your interactions with the project.\n\n~~~~~~~~~~~~~~~~~~~~\nPull Request Process\n~~~~~~~~~~~~~~~~~~~~\n\nPlease consider the following criterions in order to help us in a better way:\n\n  * The pull request is mainly expected to be a code script suggestion or improvement.\n  * Please do NOT change the ipython files. Instead, change the corresponsing PYTHON files.\n  * A pull request related to non-code-script sections is expected to make a significant difference in the documentation. Otherwise, it is expected to be announced in the issues section.\n  * Ensure any install or build dependencies are removed before the end of the layer when doing a build and creating a pull request.\n  * Add comments with details of changes to the interface, this includes new environment variables, exposed ports, useful file locations and container parameters.\n  * You may merge the Pull Request in once you have the sign-off of at least one other developer, or if you do not have permission to do that, you may request the owner to merge it for you if you believe all checks are passed.\n\n\n~~~~~~~~~~~\nFinal Note\n~~~~~~~~~~~\n\nWe are looking forward to your kind feedback. Please help us to improve this open source project and make our work better.\nFor contribution, please create a pull request and we will investigate it promptly. Once again, we appreciate\nyour kind feedback and elaborate code inspections.\n\n========================\nDevelopers\n========================\n\n\n**Company**: Instill AI [`Website\n\u003Chttps:\u002F\u002Finstillai.com\u002F>`_]\n\n**Creator**: Machine Learning Mindset [`Blog\n\u003Chttps:\u002F\u002Fmachinelearningmindset.com\u002Fblog\u002F>`_, `GitHub\n\u003Chttps:\u002F\u002Fgithub.com\u002Fmachinelearningmindset>`_, `Twitter\n\u003Chttps:\u002F\u002Ftwitter.com\u002Fmachinemindset>`_]\n\n**Developer**: Amirsina Torfi [`GitHub\n\u003Chttps:\u002F\u002Fgithub.com\u002Fastorfi>`_, `Personal Website\n\u003Chttps:\u002F\u002Fastorfi.github.io\u002F>`_, `Linkedin\n\u003Chttps:\u002F\u002Fwww.linkedin.com\u002Fin\u002Famirsinatorfi\u002F>`_ ]\n","********************\n`TensorFlow 课程`_\n********************\n.. image:: https:\u002F\u002Ftravis-ci.org\u002Finstillai\u002FTensorFlow-Course.svg?branch=master\n    :target: https:\u002F\u002Ftravis-ci.org\u002Finstillai\u002FTensorFlow-Course\n.. image:: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002Fcontributions-welcome-brightgreen.svg?style=flat\n    :target: https:\u002F\u002Fgithub.com\u002Fopen-source-for-science\u002FTensorFlow-Course\u002Fpulls\n.. image:: https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Ffollow\u002Fmachinemindset.svg?label=Follow&style=social\n    :target: https:\u002F\u002Ftwitter.com\u002Fmachinemindset\n.. image:: https:\u002F\u002Fzenodo.org\u002Fbadge\u002F151300862.svg\n   :target: https:\u002F\u002Fzenodo.org\u002Fbadge\u002Flatestdoi\u002F151300862\n\n\n本仓库旨在为 TensorFlow 提供简单易用的教程。每个教程都包含“源代码”，并且大多数教程还配有“文档”。\n\n.. .. image:: _img\u002Fmainpage\u002FTensorFlow_World.gif\n\n.. 链接。\n.. _TensorFlow: https:\u002F\u002Fwww.tensorflow.org\u002Finstall\u002F\n.. _维基百科: https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTensorFlow\u002F\n\n\n##########################################################################\n赞助\n##########################################################################\n\n为了支持本项目的维护与升级，请您考虑 `赞助项目开发者 \u003Chttps:\u002F\u002Fgithub.com\u002Fsponsors\u002Fastorfi\u002Fdashboard>`_。\n\n无论何种程度的支持，都是对本项目的重要贡献 :heart:\n\n**状态:** *本项目已更新至 **TensorFlow 2.3**。*\n\n\n#################\n目录\n#################\n.. contents::\n  :local:\n  :depth: 3\n\n\n==========================================\n免费下载 TensorFlow 路线图电子书\n==========================================\n\n.. raw:: html\n\n   \u003Cdiv align=\"center\">\n\n.. raw:: html\n\n \u003Ca href=\"http:\u002F\u002Fwww.machinelearningmindset.com\u002Ftensorflow-roadmap-ebook\u002F\" target=\"_blank\">\n  \u003Cimg width=\"710\" height=\"500\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_1bb1678f6fbd.png\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n==========================================\nSlack 社区\n==========================================\n\n.. raw:: html\n\n   \u003Cdiv align=\"center\">\n\n.. raw:: html\n\n \u003Ca href=\"https:\u002F\u002Fwww.machinelearningmindset.com\u002Fslack-group\u002F\" target=\"_blank\">\n  \u003Cimg width=\"1033\" height=\"350\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_2f07fac72f26.png\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n\n\n~~~~~~~~~~~~~~~~~~~~~\n什么是 TensorFlow？\n~~~~~~~~~~~~~~~~~~~~~\nTensorFlow 是一个开源软件库，用于在各种任务中进行数据流编程。它是一个符号数学库，也广泛应用于机器学习领域，例如神经网络。TensorFlow 在 Google 的研究和生产中都有广泛应用，常常取代其闭源前身 DistBelief。\n\nTensorFlow 由 Google Brain 团队开发，最初仅供 Google 内部使用。2015年11月9日，TensorFlow 以 Apache 2.0 开源许可证正式发布。\n\n\n============\n动机\n============\n\n这个开源项目有多种动机。截至本文撰写时，TensorFlow 是目前最好的深度学习框架之一。那么问题来了：既然网上已经有许多关于 TensorFlow 的教程，为什么还要创建这样一个仓库呢？\n\n~~~~~~~~~~~~~~~~~~~~~\n为什么使用 TensorFlow？\n~~~~~~~~~~~~~~~~~~~~~\n\n如今，深度学习备受关注——快速且优化的算法和架构实现显得尤为重要。TensorFlow 正是为此而设计的。\n\nTensorFlow 的一大优势在于其灵活性，能够构建高度模块化的模型。然而，对于初学者来说，这也可能成为劣势，因为构建模型时需要综合考虑许多细节。\n\n不过，这一问题已通过开发高级 API 得到缓解，例如 `Keras \u003Chttps:\u002F\u002Fkeras.io\u002F>`_ 和 `Slim \u003Chttps:\u002F\u002Fgithub.com\u002Ftensorflow\u002Fmodels\u002Fblob\u002F031a5a4ab41170d555bc3e8f8545cf9c8e3f1b28\u002Fresearch\u002Finception\u002Finception\u002Fslim\u002FREADME.md>`_，它们抽象了机器学习算法设计中的许多复杂部分。\n\n有趣的是，TensorFlow **如今几乎无处不在**。许多研究人员和开发者都在使用它，它的社区正以惊人的速度增长！由于参与者众多，遇到的问题往往也是其他人经常碰到的，因此解决起来相对容易。\n\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n这个仓库的意义何在？\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n**仅仅为了开发而开发开源项目，并不是我们开展这项工作的初衷。** \n考虑到大量教程不断涌入这个庞大的社区，本仓库的建立是为了打破大多数开源项目常见的“启动后很快停滞”的现象，但究竟是为什么、又该如何做到呢？\n\n首先，如果大多数人不会驻足浏览，那投入精力又有何意义？如果所创建的内容无法帮助开发者和研究人员群体，又有什么价值呢？为何要花费时间去做一件很容易被遗忘的事情呢？那么，**我们究竟该如何做呢？** 即便到目前为止，关于 TensorFlow 的教程多不胜数，涵盖模型设计和 TensorFlow 工作流程等方面。\n\n然而，其中大多数要么过于复杂，要么缺乏足够的文档说明。真正简洁明了、结构清晰，并能深入讲解具体实现模型的教程却寥寥无几。 \n\n本项目的目标是通过结构化、简洁且优化的代码实现，为社区提供更好的指导，帮助大家更快速有效地使用 TensorFlow。\n\n值得注意的是，**本项目的核心目标是提供文档完善、代码简化的教程！** \n\n=================================================\nTensorFlow 安装与环境配置\n=================================================\n\n\n.. image:: _img\u002Fmainpage\u002Finstallation-logo.gif\n   :height: 100px\n   :width: 200 px\n   :scale: 50 %\n   :alt: 替代文本\n   :align: 右侧\n   :target: docs\u002Ftutorials\u002Finstallation\n\n.. _TensorFlow 安装: https:\u002F\u002Fwww.tensorflow.org\u002Finstall\n\n要安装 TensorFlow，请参考以下链接：\n\n  * `TensorFlow 安装`_\n\n\n.. image:: _img\u002Fmainpage\u002Finstallation.gif\n    :target: https:\u002F\u002Fwww.tensorflow.org\u002Finstall\n\n建议使用虚拟环境进行安装，以避免包冲突并方便自定义工作环境。\n\n====================\nTensorFlow 教程\n====================\n\n本仓库中的教程按相关类别划分。\n\n==========================\n\n~~~~~~~~\n热身\n~~~~~~~~\n\n.. image:: _img\u002Fmainpage\u002Fwelcome.gif\n   :height: 100px\n   :width: 200 px\n   :scale: 50 %\n   :alt: 替代文本\n   :align: right\n\n\n.. _colab: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F0-welcome\u002Fwelcome.ipynb\n.. _Documentationcnnwelcome: docs\u002Ftutorials\u002F0-welcome\n.. _ipythonwelcome: codes\u002Fipython\u002F0-welcome\u002Fwelcome.ipynb\n.. _pythonwelcome: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002F0-welcome\u002Fwelcome.py\n.. _videowelcome: https:\u002F\u002Fyoutu.be\u002Fxd0DVygHlNE\n\n\n.. |Welcome| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n   :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F0-welcome\u002Fwelcome.ipynb\n\n.. |youtubeim| image:: _img\u002Fmainpage\u002FYouTube.png\n  :target: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002F_img\u002Fmainpage\u002FYouTube.png\n\n\n+----+---------------------+--------------------------+------------------------------------------------------------------------+-------------------------------------------+\n| #  |       主题         |          运行             |  源代码                                                           |  媒体                                    |\n+====+=====================+==========================+========================================================================+===========================================+\n| 1  | 启动                |       |Welcome|          | `Notebook \u003Cipythonwelcome_>`_  \u002F `Python \u003Cpythonwelcome_>`_            | `视频教程 \u003Cvideowelcome_>`_         |\n+----+---------------------+--------------------------+------------------------------------------------------------------------+-------------------------------------------+\n\n==========================\n\n~~~~~~\n基础\n~~~~~~\n\n.. raw:: html\n\n   \u003Cdiv align=\"left\">\n\n.. raw:: html\n\n \u003Ca href=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_335b3d0951a1.gif\" target=\"_blank\">\n  \u003Cimg width=\"250\" height=\"250\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_335b3d0951a1.gif\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n.. raw:: html\n\n   \u003Cbr>\n\n\n\n.. _ipythontensors: codes\u002Fipython\u002F1-basics\u002Ftensors.ipynb\n.. _pythontensors: codes\u002Fpython\u002F1-basics\u002Ftensors.py\n.. _videotensors: https:\u002F\u002Fyoutu.be\u002FOd-VvnYUbFw\n.. |Tensors| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F1-basics\u002Ftensors.ipynb\n\n.. _ipythonad: codes\u002Fipython\u002F1-basics\u002Fautomatic_differentiation.ipynb\n.. _pythonad: codes\u002Fpython\u002F1-basics\u002Fautomatic_differentiation.py\n.. _videoad: https:\u002F\u002Fyoutu.be\u002Fl-MGydWW-UE\n.. |AD| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F1-basics\u002Fautomatic_differentiation.ipynb\n\n.. _ipythongraphs: codes\u002Fipython\u002F1-basics\u002Fgraph.ipynb\n.. _pythongraphs: codes\u002Fpython\u002F1-basics\u002Fgraph.py\n.. _videographs: https:\u002F\u002Fyoutu.be\u002FP9xA1s6AUNk\n.. |graphs| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F1-basics\u002Fgraph.ipynb\n\n\n.. _ipythonmodels: codes\u002Fipython\u002F1-basics\u002Fmodels.ipynb\n.. _pythonmodels: codes\u002Fpython\u002F1-basics\u002Fmodels.py\n.. _videomodels: https:\u002F\u002Fyoutu.be\u002FWnlUE04REOY\n.. |models| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002F1-basics\u002Fmodels.ipynb\n\n\n\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------+-----------------------------------------+\n| #  |       主题                       |          运行             |  源代码                                                           |        媒体                            |\n+====+===================================+==========================+========================================================================+=========================================+\n| 1  | 张量                           |       |Tensors|          | `Notebook \u003Cipythontensors_>`_  \u002F `Python \u003Cpythontensors_>`_            | `视频教程 \u003Cvideotensors_>`_       |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------+-----------------------------------------+\n| 2  | 自动微分                         |       |AD|               | `Notebook \u003Cipythonad_>`_  \u002F `Python \u003Cpythonad_>`_                      | `视频教程 \u003Cvideoad_>`_            |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------+-----------------------------------------+\n| 3  | 图简介                           |       |graphs|           | `Notebook \u003Cipythongraphs_>`_ \u002F `Python \u003Cpythongraphs_>`_               | `视频教程 \u003Cvideographs_>`_        |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------+-----------------------------------------+\n| 4  | TensorFlow 模型                 |       |models|           | `Notebook \u003Cipythonmodels_>`_  \u002F `Python \u003Cpythonmodels_>`_              | `视频教程 \u003Cvideomodels_>`_        |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------+-----------------------------------------+\n\n==========================\n\n~~~~~~~~~~~~~~~~~~~~~~\n基础机器学习\n~~~~~~~~~~~~~~~~~~~~~~\n\n.. raw:: html\n\n   \u003Cdiv align=\"left\">\n\n.. raw:: html\n\n \u003Ca href=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_75e92653de4e.gif\" target=\"_blank\">\n  \u003Cimg width=\"250\" height=\"250\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_75e92653de4e.gif\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n.. raw:: html\n\n   \u003Cbr>\n\n.. .. image:: _img\u002Fmainpage\u002Fbasicmodels.gif\n..    :height: 100px\n..    :width: 200 px\n..    :scale: 50 %\n..    :alt: 替代文本\n..    :align: right\n\n\n.. _ipythonlinearreg: codes\u002Fipython\u002Fbasics_in_machine_learning\u002Flinearregression.ipynb\n.. _pythonlinearreg: codes\u002Fpython\u002Fbasics_in_machine_learning\u002Flinearregression.py\n.. _tutoriallinearreg: https:\u002F\u002Fwww.machinelearningmindset.com\u002Flinear-regression-with-tensorflow\u002F\n.. _videoinearreg: https:\u002F\u002Fyoutu.be\u002F2RTBBiKKuLI\n\n.. _tutorialdataaugmentation: https:\u002F\u002Fwww.machinelearningmindset.com\u002Fdata-augmentation-with-tensorflow\u002F\n.. _ipythondataaugmentation: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fbasics_in_machine_learning\u002Fdataaugmentation.ipynb\n.. _pythondataaugmentation: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fbasics_in_machine_learning\u002Fdataaugmentation.py\n.. _videodataaugmentation: https:\u002F\u002Fyoutu.be\u002FHbzR2snHJF0\n\n.. |lr| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fbasics_in_machine_learning\u002Flinearregression.ipynb\n.. |da| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fbasics_in_machine_learning\u002Fdataaugmentation.ipynb\n\n\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------------------+----------------------------------------------+----------------------------------------------+\n| #  |       topic                       |          Run             |  Source Code                                                                       |  More                                        |           Media                              |\n+====+===================================+==========================+====================================================================================+==============================================+==============================================+\n| 1  | 线性回归                          |       |lr|               | `笔记本 \u003Cipythonlinearreg_>`_  \u002F `Python \u003Cpythonlinearreg_>`_                    | `教程 \u003Ctutoriallinearreg_>`_             | `视频教程 \u003Cvideoinearreg_>`_           |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------------------+----------------------------------------------+----------------------------------------------+\n| 2  | 数据增强                          |       |da|               | `笔记本 \u003Cipythondataaugmentation_>`_ \u002F `Python \u003Cpythondataaugmentation_>`_       | `教程 \u003Ctutorialdataaugmentation_>`_      | `视频教程 \u003Cvideodataaugmentation_>`_   |\n+----+-----------------------------------+--------------------------+------------------------------------------------------------------------------------+----------------------------------------------+----------------------------------------------+\n\n\n\n.. +----+----------------------------+----------------------------------------------------------------------------------------+----------------------------------------------+\n\n==========================\n\n~~~~~~~~~~~~~~~~\n神经网络\n~~~~~~~~~~~~~~~~\n\n.. raw:: html\n\n   \u003Cdiv align=\"left\">\n\n.. raw:: html\n\n \u003Ca href=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_4159e99af90c.png\" target=\"_blank\">\n  \u003Cimg width=\"600\" height=\"180\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_4159e99af90c.png\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n.. raw:: html\n\n    \u003Cbr>\n\n\n.. _ipythonmlp: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fneural_networks\u002Fmlp.ipynb\n.. _pythonmlp: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fneural_networks\u002Fmlp.py\n.. _videomlp: https:\u002F\u002Fyoutu.be\u002Fw20efZqSK2Y\n\n.. _ipythoncnn: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fneural_networks\u002FCNNs.ipynb\n.. _pythoncnn: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fneural_networks\u002Fcnns.py\n.. _videocnn: https:\u002F\u002Fyoutu.be\u002FWVifZBCRz8g\n\n\n.. |mlp| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fneural_networks\u002Fmlp.ipynb\n.. |cnn| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fneural_networks\u002FCNNs.ipynb\n\n\n+----+------------------------------------------+--------------------------+------------------------------------------------------+------------------------------------+\n| #  |       topic                              |          Run             |  Source Code                                         |            Media                   |\n+====+==========================================+==========================+======================================================+====================================+\n| 1  |  *多层感知机*                           |       |mlp|              | `笔记本 \u003Cipythonmlp_>`_ \u002F `Python \u003Cpythonmlp_>`_   | `视频教程 \u003Cvideomlp_>`_      |\n+----+------------------------------------------+--------------------------+------------------------------------------------------+------------------------------------+\n| 2  |  *卷积神经网络*                         |       |cnn|              | `笔记本 \u003Cipythoncnn_>`_ \u002F `Python \u003Cpythoncnn_>`_   | `视频教程 \u003Cvideocnn_>`_      |\n+----+------------------------------------------+--------------------------+------------------------------------------------------+------------------------------------+\n\n==========================\n\n~~~~~~~~~~~~~~~~\n进阶\n~~~~~~~~~~~~~~~~\n\n\n.. raw:: html\n\n   \u003Cdiv align=\"left\">\n\n.. raw:: html\n\n \u003Ca href=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_59783e4cb798.png\" target=\"_blank\">\n  \u003Cimg width=\"180\" height=\"180\" align=\"center\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_readme_59783e4cb798.png\"\u002F>\n \u003C\u002Fa>\n\n.. raw:: html\n\n   \u003C\u002Fdiv>\n\n.. raw:: html\n\n    \u003Cbr>\n\n\n\n\n.. _ipythoncustomtr: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Fcustom_training.ipynb\n.. _pythoncustomtr: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fadvanced\u002Fcustom_training.py\n.. _videocustomtr: https:\u002F\u002Fyoutu.be\u002Fz5gcabfyPfA\n\n.. _ipythondgenerator: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Fdataset_generator.ipynb\n.. _pythondgenerator: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fadvanced\u002Fdataset_generator.py\n.. _videodgenerator: https:\u002F\u002Fyoutu.be\u002F-YsgMdDPu3g\n\n.. _ipythontfrecords: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Ftfrecords.ipynb\n.. _pythontfrecords: https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fpython\u002Fadvanced\u002Ftfrecords.py\n.. _videotfrecords: https:\u002F\u002Fyoutu.be\u002Fzqavy_5QMk8\n\n\n.. |ctraining| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Fcustom_training.ipynb\n\n.. |dgenerator| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Fdataset_generator.ipynb\n\n.. |tfrecords| image:: https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\n  :target: https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Finstillai\u002FTensorFlow-Course\u002Fblob\u002Fmaster\u002Fcodes\u002Fipython\u002Fadvanced\u002Ftfrecords.ipynb\n\n\n+----+------------------------------------------+--------------------------+--------------------------------------------------------------------+----------------------------------------+\n| #  |       主题                              |          运行            |  源代码                                                           |           媒体                        |\n+====+==========================================+==========================+====================================================================+========================================+\n| 1  |  *自定义训练*                           |       |ctraining|        | `笔记本 \u003Cipythoncustomtr_>`_ \u002F `Python \u003Cpythoncustomtr_>`_       | `视频教程 \u003Cvideocustomtr_>`_           |\n+----+------------------------------------------+--------------------------+--------------------------------------------------------------------+----------------------------------------+\n| 2  |  *数据集生成器*                         |       |dgenerator|       | `笔记本 \u003Cipythondgenerator_>`_ \u002F `Python \u003Cpythondgenerator_>`_   | `视频教程 \u003Cvideodgenerator_>`_         |\n+----+------------------------------------------+--------------------------+--------------------------------------------------------------------+----------------------------------------+\n| 3  |  *创建 TFRecords*                       |       |tfrecords|        | `笔记本 \u003Cipythontfrecords_>`_ \u002F `Python \u003Cpythontfrecords_>`_     | `视频教程 \u003Cvideotfrecords_>`_          |\n+----+------------------------------------------+--------------------------+--------------------------------------------------------------------+----------------------------------------+\n\n\n\n=====================\n一些有用的教程\n=====================\n\n  * `TensorFlow 示例 \u003Chttps:\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 \u003Chttps:\u002F\u002Fgithub.com\u002Fsjchoi86\u002FTensorflow-101>`_ - 使用 Jupyter Notebook 编写的 Python 版 TensorFlow 教程\n  * `Terry Um 的 TensorFlow 练习 \u003Chttps:\u002F\u002Fgithub.com\u002Fterryum\u002FTensorFlow_Exercises>`_ - 重新实现其他 TensorFlow 示例中的代码\n  * `时间序列分类 \u003Chttps:\u002F\u002Fgithub.com\u002Fguillaume-chevalier\u002FLSTM-Human-Activity-Recognition>`_ - 在手机传感器数据上使用 LSTM 的 TensorFlow 循环神经网络分类\n\n\n=============\n贡献说明\n=============\n\n在向本仓库提交贡献时，请先通过议题、邮件或其他方式与仓库所有者讨论您希望进行的更改，再进行实际修改。*对于错别字，请勿直接创建拉取请求，而应在议题中指出或直接联系仓库所有者*。\n\n请注意，我们有一份行为准则，请在与本项目的所有互动中严格遵守。\n\n~~~~~~~~~~~~~~~~~~~~\n拉取请求流程\n~~~~~~~~~~~~~~~~~~~~\n\n为便于我们更好地处理您的请求，请参考以下标准：\n\n  * 拉取请求主要应围绕代码脚本的建议或改进。\n  * 请勿直接修改 ipython 文件，而是修改对应的 PYTHON 文件。\n  * 如果拉取请求涉及非代码部分，则需对文档产生显著影响；否则，建议在议题中提出。\n  * 在构建并提交拉取请求之前，请确保已移除所有安装或构建依赖项。\n  * 对界面变更添加详细注释，包括新增环境变量、开放端口、重要文件路径及容器参数等。\n  * 当至少有一位其他开发者签字确认后，您可以自行合并拉取请求；若无权限，可在确认所有检查均已通过的情况下，请求仓库所有者代为合并。\n\n\n~~~~~~~~~~~\n最后说明\n~~~~~~~~~~~\n\n我们期待您的宝贵反馈！请帮助我们改进这个开源项目，使我们的工作更加完善。如需贡献，请创建一个拉取请求，我们将尽快予以审核。再次感谢您的反馈与细致的代码审查。\n\n========================\n开发者\n========================\n\n\n**公司**: Instill AI [`官网\n\u003Chttps:\u002F\u002Finstillai.com\u002F>`_]\n\n**创建者**: Machine Learning Mindset [`博客\n\u003Chttps:\u002F\u002Fmachinelearningmindset.com\u002Fblog\u002F>`_, `GitHub\n\u003Chttps:\u002F\u002Fgithub.com\u002Fmachinelearningmindset>`_, `Twitter\n\u003Chttps:\u002F\u002Ftwitter.com\u002Fmachinemindset>`_]\n\n**开发者**: Amirsina Torfi [`GitHub\n\u003Chttps:\u002F\u002Fgithub.com\u002Fastorfi>`_, `个人网站\n\u003Chttps:\u002F\u002Fastorfi.github.io\u002F>`_, `LinkedIn\n\u003Chttps:\u002F\u002Fwww.linkedin.com\u002Fin\u002Famirsinatorfi\u002F>`_ ]","# TensorFlow-Course 快速上手指南\n\n本指南基于 `TensorFlow-Course` 开源项目整理，旨在帮助开发者快速掌握 TensorFlow 2.3+ 的核心概念与实战代码。该项目提供结构清晰、文档完善的教程源码。\n\n## 环境准备\n\n在开始之前，请确保您的开发环境满足以下要求：\n\n*   **操作系统**：Linux, macOS, 或 Windows (WSL 推荐)\n*   **Python 版本**：Python 3.6 - 3.8 (兼容 TensorFlow 2.3)\n*   **前置依赖**：\n    *   `pip` (Python 包管理工具)\n    *   `git` (用于克隆代码仓库)\n    *   建议创建虚拟环境以避免包冲突 (如 `venv` 或 `conda`)\n\n> **提示**：本项目已更新至 **TensorFlow 2.3** 版本，建议使用对应版本的 Python 环境以获得最佳兼容性。\n\n## 安装步骤\n\n### 1. 克隆项目代码\n首先，将教程源码克隆到本地：\n\n```bash\ngit clone https:\u002F\u002Fgithub.com\u002Fopen-source-for-science\u002FTensorFlow-Course.git\ncd TensorFlow-Course\n```\n\n### 2. 创建并激活虚拟环境\n推荐使用虚拟环境隔离依赖。\n\n**使用 venv:**\n```bash\npython3 -m venv tf_env\nsource tf_env\u002Fbin\u002Factivate  # Windows 用户请使用: tf_env\\Scripts\\activate\n```\n\n**使用 conda:**\n```bash\nconda create -n tf_env python=3.7\nconda activate tf_env\n```\n\n### 3. 安装 TensorFlow 及依赖\n国内用户建议使用清华或阿里镜像源加速安装。\n\n**安装 TensorFlow 2.3 (CPU 版本):**\n```bash\npip install tensorflow==2.3.0 -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n\n**安装其他必要依赖 (如果 requirements.txt 存在):**\n```bash\npip install -r requirements.txt -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n```\n\n> **注意**：如果需要 GPU 支持，请确保已安装对应的 NVIDIA 驱动和 CUDA Toolkit，然后安装 `tensorflow-gpu==2.3.0`。\n\n## 基本使用\n\n本项目教程分为多个模块，从基础热身到高级模型。以下是运行第一个\"热身\"教程的示例。\n\n### 方式一：使用 Jupyter Notebook (推荐)\n项目提供了 `.ipynb` 文件，适合交互式学习。\n\n1.  安装 Jupyter：\n    ```bash\n    pip install notebook -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n    ```\n2.  启动 Notebook 并打开热身教程：\n    ```bash\n    jupyter notebook codes\u002Fipython\u002F0-welcome\u002Fwelcome.ipynb\n    ```\n3.  在浏览器中依次点击单元格运行代码，观察输出结果。\n\n### 方式二：直接运行 Python 脚本\n如果您偏好命令行执行，可以直接运行对应的 `.py` 文件。\n\n**运行热身示例：**\n```bash\npython codes\u002Fpython\u002F0-welcome\u002Fwelcome.py\n```\n\n**运行基础张量 (Tensors) 示例：**\n```bash\npython codes\u002Fpython\u002F1-basics\u002Ftensors.py\n```\n\n### 核心代码示例解析\n在 `welcome.py` 或 `tensors.py` 中，您将看到典型的 TensorFlow 2.x 代码风格（Eager Execution）：\n\n```python\nimport tensorflow as tf\n\n# 创建一个简单的张量\nhello = tf.constant('Hello, TensorFlow!')\n\n# 直接打印张量内容 (TF 2.x 默认开启即时执行)\nprint(hello.numpy())\n\n# 定义一个简单的数学运算\na = tf.constant(10)\nb = tf.constant(20)\nc = tf.add(a, b)\n\nprint(f\"Result: {c.numpy()}\")\n```\n\n### 在线体验 (无需本地安装)\n如果暂时无法配置本地环境，项目支持直接在 Google Colab 上运行所有教程。\n访问任意教程目录下的 `.ipynb` 文件链接（例如 GitHub 页面上的 \"Open in Colab\" 按钮），即可在云端免费运行代码。","某初创公司的算法实习生小李，需要在两周内基于 TensorFlow 2.3 构建一个图像分类原型以向投资人演示，但他对框架的模块化设计感到无从下手。\n\n### 没有 TensorFlow-Course 时\n- **环境配置受阻**：面对 TensorFlow 频繁的版本迭代，花费数天排查依赖冲突，始终无法在本地跑通\"Hello World\"级别的示例代码。\n- **概念理解割裂**：官方文档过于侧重底层数学原理和分散的 API 说明，难以将张量运算、会话管理等碎片化知识串联成完整的模型构建逻辑。\n- **试错成本高昂**：由于缺乏结构化的参考代码，每次调整网络架构都需要从头编写大量样板代码，调试过程如同“盲人摸象”，严重拖慢开发进度。\n- **实战信心不足**：面对高度灵活的模块化设计，担心因架构设计不当导致模型无法收敛，迟迟不敢动手实现核心业务逻辑。\n\n### 使用 TensorFlow-Course 后\n- **快速启动项目**：直接复用仓库中已适配 TensorFlow 2.3 的现成教程代码，几分钟内即可完成环境验证并运行起第一个基准模型。\n- **体系化学习路径**：跟随由浅入深的教程目录，通过关联的源码与文档对照，迅速理清了从数据加载到模型训练的全流程逻辑。\n- **高效迭代优化**：基于提供的标准源代码进行修改和扩展，将原本需要数天的架构调整工作缩短至几小时，专注于业务算法本身的优化。\n- **落地底气增强**：参考成熟的案例实现，不仅规避了常见的入门陷阱，还快速构建出稳定的演示原型，顺利完成了投资汇报。\n\nTensorFlow-Course 通过将复杂的框架细节封装为即插即用的教程与源码，极大地降低了深度学习的学习门槛与工程落地成本。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Finstillai_TensorFlow-Course_5839e8f5.png","instillai","Instill AI","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Finstillai_db38df51.png","A company offering AI-based solutions to real-world applications.",null,"contact@instillai.com","https:\u002F\u002Finstillai.com","https:\u002F\u002Fgithub.com\u002Finstillai",[81,85,88],{"name":82,"color":83,"percentage":84},"Jupyter Notebook","#DA5B0B",96.9,{"name":86,"color":87,"percentage":10},"Python","#3572A5",{"name":89,"color":90,"percentage":91},"Shell","#89e051",0,16307,3152,"2026-04-15T03:30:56","MIT","未说明",{"notes":98,"python":96,"dependencies":99},"该项目已更新至 TensorFlow 2.3 版本。README 建议使用虚拟环境（virtual environment）进行安装，以防止包冲突并自定义工作环境。教程提供 Notebook 和 Python 脚本两种形式，支持在 Google Colab 上直接运行。",[100,101],"TensorFlow>=2.3","Keras (内置于 TensorFlow)",[14],[104,105,106,107],"tensorflow","python","deep-learning","deep-learning-tutorial","2026-03-27T02:49:30.150509","2026-04-18T17:04:04.969144",[111,116,121,126,131,136],{"id":112,"question_zh":113,"answer_zh":114,"source_url":115},40461,"这个教程兼容哪个版本的 TensorFlow？","本教程最初是基于 TensorFlow 1.x 编写的。如果您使用的是 TensorFlow 2.x（如 2.2.0），直接运行会报错（例如找不到 `tf.Session`）。解决方法是添加兼容性代码：\n```python\nimport tensorflow.compat.v1 as tf\ntf.disable_v2_behavior()\n```\n维护者曾计划在未来将教程升级至 TF 2.2，但当前主要仍适用于 TF 1.x 环境或通过兼容模式运行。","https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fissues\u002F23",{"id":117,"question_zh":118,"answer_zh":119,"source_url":120},40462,"课程开始前需要掌握哪些前置知识？","学习本课程的主要前置要求是具备数学基础（Math），特别是线性代数和微积分相关知识，以便理解模型背后的数学原理。","https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fissues\u002F2",{"id":122,"question_zh":123,"answer_zh":124,"source_url":125},40463,"是否有计划升级到 TensorFlow 2.0 或更高版本？","是的，维护者有计划在 TensorFlow 2.0 版本稳定后不久升级代码库。对于 TF 2.2 的教程转换计划曾在 2020 年 9 月左右提出。目前建议用户通过 `tensorflow.compat.v1` 模块在 TF 2.x 环境中运行现有代码。","https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fissues\u002F29",{"id":127,"question_zh":128,"answer_zh":129,"source_url":130},40464,"Linear SVM 实现中的 norm_term 计算方式是否正确？","关于 `norm_term` 的计算存在讨论。原始代码使用 `tf.reduce_sum(tf.multiply(tf.transpose(W),W))`。虽然 `tf.matmul` 也可以用于计算，但由于权重 W 的形状通常为 Nx1，`tf.matmul` 的结果是 1x1 矩阵，而 `tf.reduce_sum` 能更明确地将其转换为标量并求和。维护者认为保留 `tf.reduce_sum` 是更好的做法，以确保数值计算的准确性。","https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fissues\u002F30",{"id":132,"question_zh":133,"answer_zh":134,"source_url":135},40465,"如何修复文档中的拼写错误或代码变量名错误？","如果您发现文档中有拼写错误（如 'it' 应为 'its'）或代码中的变量名错误（如 `all_variables_list` 应为 `variable_list_custom`），维护者欢迎社区贡献。请直接提交 Pull Request (PR) 进行修正，并在 Issue 中说明情况，维护者审核后会合并。","https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fissues\u002F20",{"id":137,"question_zh":138,"answer_zh":139,"source_url":140},40466,"运行代码时遇到 `int(np.power(2, 7))` 相关的问题如何解决？","在 `logistic_regression.py` 第 29 行和 `train_mlp.py` 第 26 行，如果涉及数据类型或形状问题，建议显式地将结果转换为整数类型，即使用 `int(np.power(2, 7))` 来确保参数类型正确。如果遇到此类运行问题，欢迎提交 Pull Request 修复。","https:\u002F\u002Fgithub.com\u002Finstillai\u002FTensorFlow-Course\u002Fissues\u002F28",[142],{"id":143,"version":144,"summary_zh":76,"released_at":145},323889,"1.0","2019-12-19T17:48:04"]