[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71859":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":9,"language":10,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":15,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":9,"pushedAt":9,"updatedAt":26,"readmeContent":27,"aiSummary":28,"trendingCount":15,"starSnapshotCount":15,"syncStatus":29,"lastSyncTime":30,"discoverSource":31},71859,"handson-ml3","ageron\u002Fhandson-ml3","ageron","A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.",null,"Jupyter Notebook",13416,5136,195,68,0,40,88,272,120,45,"Apache License 2.0",false,"main",true,[],"2026-06-12 02:02:55","Machine Learning Notebooks, 3rd edition\n=================================\n\nThis project aims at teaching you the fundamentals of Machine Learning in\npython. It contains the example code and solutions to the exercises in the third edition of my O'Reilly book [Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow (3rd edition)](https:\u002F\u002Fhoml.info\u002Fer3):\n\n\u003Ca href=\"https:\u002F\u002Fhoml.info\u002Fer3\">\u003Cimg src=\"https:\u002F\u002Flearning.oreilly.com\u002Flibrary\u002Fcover\u002F9781098125967\u002F300w\u002F\" title=\"book\" width=\"150\" border=\"0\" \u002F>\u003C\u002Fa>\n\n**Note**: If you are looking for the second edition notebooks, check out [ageron\u002Fhandson-ml2](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml2). For the first edition, see [ageron\u002Fhandson-ml](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml).\n\n## Quick Start\n\n### Want to play with these notebooks online without having to install anything?\n\n* \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fageron\u002Fhandson-ml3\u002Fblob\u002Fmain\u002F\" target=\"_parent\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" alt=\"Open In Colab\"\u002F>\u003C\u002Fa> (recommended)\n\n⚠ _Colab provides a temporary environment: anything you do will be deleted after a while, so make sure you download any data you care about._\n\n\u003Cdetails>\n\nOther services may work as well, but I have not fully tested them:\n\n* \u003Ca href=\"https:\u002F\u002Fhoml.info\u002Fkaggle3\u002F\">\u003Cimg src=\"https:\u002F\u002Fkaggle.com\u002Fstatic\u002Fimages\u002Fopen-in-kaggle.svg\" alt=\"Open in Kaggle\" \u002F>\u003C\u002Fa>\n\n* \u003Ca href=\"https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fageron\u002Fhandson-ml3\u002FHEAD?filepath=%2Findex.ipynb\">\u003Cimg src=\"https:\u002F\u002Fmybinder.org\u002Fbadge_logo.svg\" alt=\"Launch binder\" \u002F>\u003C\u002Fa>\n\n* \u003Ca href=\"https:\u002F\u002Fhoml.info\u002Fdeepnote3\u002F\">\u003Cimg src=\"https:\u002F\u002Fdeepnote.com\u002Fbuttons\u002Flaunch-in-deepnote-small.svg\" alt=\"Launch in Deepnote\" \u002F>\u003C\u002Fa>\n\n\u003C\u002Fdetails>\n\n### Just want to quickly look at some notebooks, without executing any code?\n\n* \u003Ca href=\"https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fageron\u002Fhandson-ml3\u002Fblob\u002Fmain\u002Findex.ipynb\">\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fjupyter\u002Fdesign\u002Fmaster\u002Flogos\u002FBadges\u002Fnbviewer_badge.svg\" alt=\"Render nbviewer\" \u002F>\u003C\u002Fa>\n\n* [github.com's notebook viewer](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml3\u002Fblob\u002Fmain\u002Findex.ipynb) also works but it's not ideal: it's slower, the math equations are not always displayed correctly, and large notebooks often fail to open.\n\n### Want to run this project using a Docker image?\nRead the [Docker instructions](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml3\u002Ftree\u002Fmain\u002Fdocker).\n\n### Want to install this project on your own machine?\n\nStart by installing [Anaconda](https:\u002F\u002Fwww.anaconda.com\u002Fproducts\u002Fdistribution) (or [Miniconda](https:\u002F\u002Fdocs.conda.io\u002Fen\u002Flatest\u002Fminiconda.html)), [git](https:\u002F\u002Fgit-scm.com\u002Fdownloads), and if you have a TensorFlow-compatible GPU, install the [GPU driver](https:\u002F\u002Fwww.nvidia.com\u002FDownload\u002Findex.aspx), as well as the appropriate version of CUDA and cuDNN (see TensorFlow's documentation for more details).\n\nNext, clone this project by opening a terminal and typing the following commands (do not type the first `$` signs on each line, they just indicate that these are terminal commands):\n\n    $ git clone https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml3.git\n    $ cd handson-ml3\n\nNext, run the following commands:\n\n    $ conda env create -f environment.yml\n    $ conda activate homl3\n    $ python -m ipykernel install --user --name=python3\n\nFinally, start Jupyter:\n\n    $ jupyter notebook\n\nIf you need further instructions, read the [detailed installation instructions](INSTALL.md).\n\n# FAQ\n\n**Which Python version should I use?**\n\nI recommend Python 3.10. If you follow the installation instructions above, that's the version you will get. Any version ≥3.7 should work as well.\n\n**I'm getting an error when I call `load_housing_data()`**\n\nIf you're getting an HTTP error, make sure you're running the exact same code as in the notebook (copy\u002Fpaste it if needed). If the problem persists, please check your network configuration. If it's an SSL error, see the next question.\n\n**I'm getting an SSL error on MacOSX**\n\nYou probably need to install the SSL certificates (see this [StackOverflow question](https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F27835619\u002Furllib-and-ssl-certificate-verify-failed-error)). If you downloaded Python from the official website, then run `\u002FApplications\u002FPython\\ 3.10\u002FInstall\\ Certificates.command` in a terminal (change `3.10` to whatever version you installed). If you installed Python using MacPorts, run `sudo port install curl-ca-bundle` in a terminal.\n\n**I've installed this project locally. How do I update it to the latest version?**\n\nSee [INSTALL.md](INSTALL.md)\n\n**How do I update my Python libraries to the latest versions, when using Anaconda?**\n\nSee [INSTALL.md](INSTALL.md)\n\n## Contributors\nI would like to thank everyone [who contributed to this project](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml3\u002Fgraphs\u002Fcontributors), either by providing useful feedback, filing issues or submitting Pull Requests. Special thanks go to Haesun Park and Ian Beauregard who reviewed every notebook and submitted many PRs, including help on some of the exercise solutions. Thanks as well to Steven Bunkley and Ziembla who created the `docker` directory, and to github user SuperYorio who helped on some exercise solutions. Thanks a lot to Victor Khaustov who submitted plenty of excellent PRs, fixing many errors. And lastly, thanks to Google ML Developer Programs team who supported this work by providing Google Cloud Credit.\n","该项目是一系列Jupyter笔记本，旨在通过使用Scikit-Learn、Keras和TensorFlow 2来指导你学习Python中的机器学习与深度学习基础。核心功能包括提供书中示例代码及练习题解答，并支持多种在线平台如Google Colab直接运行，无需本地安装任何软件。此外，还提供了Docker镜像以及详细的本地安装指南以满足不同用户需求。适用于想要系统性入门或加深对机器学习理解的开发者、学生以及研究人员，在实际项目开发前快速掌握相关技术。",2,"2026-06-11 03:38:58","high_star"]