[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-71483":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":19,"compositeScore":20,"rankGlobal":10,"rankLanguage":10,"license":21,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":22,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":26,"readmeContent":27,"aiSummary":28,"trendingCount":16,"starSnapshotCount":16,"syncStatus":29,"lastSyncTime":30,"discoverSource":31},71483,"handson-ml2","ageron\u002Fhandson-ml2","ageron","⛔️ DEPRECATED – See https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml3 or handson-mlp instead.","",null,"Jupyter Notebook",29937,13188,657,213,0,4,14,12,45,"Apache License 2.0",false,"master",true,[],"2026-06-12 02:02:53","Machine Learning Notebooks\n==========================\n\n# ⚠ THIS EDITION IS NOW OUTDATED. THE \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml3\">THIRD EDITION OF MY BOOK\u003C\u002Fa> IS NOW AVAILABLE, AS WELL A \u003Ca href=\"https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-mlp\">PYTORCH VERSION\u003C\u002Fa> AND \u003Ca href=\"https:\u002F\u002Fhoml.info\u002F\">MANY TRANSLATIONS\u003C\u002Fa>.\n\nThis project is for the second edition, which is now outdated (it came out in 2019).\n\n\u003Cdetails>\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 second edition of my O'Reilly book [Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow](https:\u002F\u002Fwww.oreilly.com\u002Flibrary\u002Fview\u002Fhands-on-machine-learning\u002F9781492032632\u002F):\n\n\u003Cimg src=\"https:\u002F\u002Fimages-na.ssl-images-amazon.com\u002Fimages\u002FI\u002F51aqYc1QyrL._SX379_BO1,204,203,200_.jpg\" title=\"book\" width=\"150\" \u002F>\n\n**Note**: If you are looking for the first edition notebooks, check out [ageron\u002Fhandson-ml](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml). For the third edition, check out [ageron\u002Fhandson-ml3](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml3).\n\n## Quick Start\n\n### Want to play with these notebooks online without having to install anything?\nUse any of the following services (I recommended Colab or Kaggle, since they offer free GPUs and TPUs).\n\n**WARNING**: _Please be aware that these services provide temporary environments: anything you do will be deleted after a while, so make sure you download any data you care about._\n\n* \u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fageron\u002Fhandson-ml2\u002Fblob\u002Fmaster\u002F\" target=\"_parent\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fassets\u002Fcolab-badge.svg\" alt=\"Open In Colab\"\u002F>\u003C\u002Fa>\n\n* \u003Ca href=\"https:\u002F\u002Fhoml.info\u002Fkaggle\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-ml2\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\u002Fdeepnote\u002F\">\u003Cimg src=\"https:\u002F\u002Fdeepnote.com\u002Fbuttons\u002Flaunch-in-deepnote-small.svg\" alt=\"Launch in Deepnote\" \u002F>\u003C\u002Fa>\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-ml2\u002Fblob\u002Fmaster\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-ml2\u002Fblob\u002Fmaster\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-ml2\u002Ftree\u002Fmaster\u002Fdocker).\n\n### Want to install this project on your own machine?\n\nStart by installing [Anaconda](https:\u002F\u002Fwww.anaconda.com\u002Fdistribution\u002F) (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-ml2.git\n    $ cd handson-ml2\n\nNext, run the following commands:\n\n    $ conda env create -f environment.yml\n    $ conda activate tf2\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 3.8. If you follow the installation instructions above, that's the version you will get. Most code will work with other versions of Python 3, but some libraries do not support Python 3.9 or 3.10 yet, which is why I recommend Python 3.8.\n\n**I'm getting an error when I call `load_housing_data()`**\n\nMake sure you call `fetch_housing_data()` *before* you call `load_housing_data()`. If 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.\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\\ 3.8\u002FInstall\\ Certificates.command` in a terminal (change `3.8` 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-ml2\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.\n\n\u003C\u002Fdetails>\n","该项目是基于Python的机器学习教程，通过Jupyter Notebook形式提供《Hands-on Machine Learning with Scikit-Learn, Keras and TensorFlow》第二版书籍中的示例代码和练习解答。核心功能包括使用Scikit-Learn、Keras以及TensorFlow框架实现多种机器学习算法，并附有详细的实践案例与理论讲解。技术特点在于其结构化的内容设计便于读者循序渐进地掌握从基础到高级的机器学习知识。尽管当前版本已不再更新，但对于初学者而言，它仍然是一个很好的入门资源，特别适合于希望快速上手并理解机器学习基本概念和技术的应用场景。此外，项目还支持在线运行环境如Google Colab等，使得学习过程更加便捷高效。",2,"2026-06-11 03:37:59","high_star"]