[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9574":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":16,"stars7d":17,"stars30d":18,"stars90d":16,"forks30d":16,"starsTrendScore":16,"compositeScore":19,"rankGlobal":10,"rankLanguage":10,"license":20,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":23,"hasPages":21,"topics":24,"createdAt":10,"pushedAt":10,"updatedAt":35,"readmeContent":36,"aiSummary":37,"trendingCount":16,"starSnapshotCount":16,"syncStatus":38,"lastSyncTime":39,"discoverSource":40},9574,"handson-ml","ageron\u002Fhandson-ml","ageron","⛔️ DEPRECATED – See https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml3 or handson-mlp instead.","",null,"Jupyter Notebook",25609,12784,1079,127,0,1,9,71.4,"Apache License 2.0",false,"master",true,[25,26,27,28,29,30,31,32,33,34],"deep-learning","deprecated","distributed","jupyter-notebook","machine-learning","ml","neural-network","python","scikit-learn","tensorflow","2026-06-12 04:00:45","Machine Learning Notebooks\n==========================\n\n# ⚠ 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.\n\nThis project is for the first edition, which is now outdated (it came out in 2017).\n\n\u003Cdetails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 my O'Reilly book [Hands-on Machine Learning with Scikit-Learn and TensorFlow](https:\u002F\u002Flearning.oreilly.com\u002Flibrary\u002Fview\u002Fhands-on-machine-learning\u002F9781491962282\u002F):\n\n[![book](http:\u002F\u002Fakamaicovers.oreilly.com\u002Fimages\u002F9781491962282\u002Fcat.gif)](https:\u002F\u002Flearning.oreilly.com\u002Flibrary\u002Fview\u002Fhands-on-machine-learning\u002F9781491962282\u002F)\n\n\n## Quick Start\n\n### Want to play with these notebooks online without having to install anything?\nUse any of the following services.\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* **Recommended**: open this repository in [Colaboratory](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fageron\u002Fhandson-ml\u002Fblob\u002Fmaster\u002F):\n\u003Ca href=\"https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Fageron\u002Fhandson-ml\u002Fblob\u002Fmaster\u002F\">\u003Cimg src=\"https:\u002F\u002Fcolab.research.google.com\u002Fimg\u002Fcolab_favicon.ico\" width=\"90\" \u002F>\u003C\u002Fa>\n\n* Or open it in [Binder](https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fageron\u002Fhandson-ml\u002Fmaster):\n\u003Ca href=\"https:\u002F\u002Fmybinder.org\u002Fv2\u002Fgh\u002Fageron\u002Fhandson-ml\u002Fmaster\">\u003Cimg src=\"https:\u002F\u002Fmatthiasbussonnier.com\u002Fposts\u002Fimg\u002Fbinder_logo_128x128.png\" width=\"90\" \u002F>\u003C\u002Fa>\n\n  * _Note_: Most of the time, Binder starts up quickly and works great, but when handson-ml is updated, Binder creates a new environment from scratch, and this can take quite some time.\n\n* Or open it in [Deepnote](https:\u002F\u002Fbeta.deepnote.com\u002Flaunch?template=data-science&url=https%3A\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml\u002Fblob\u002Fmaster\u002Findex.ipynb):\n\u003Ca href=\"https:\u002F\u002Fbeta.deepnote.com\u002Flaunch?template=data-science&url=https%3A\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml\u002Fblob\u002Fmaster\u002Findex.ipynb\">\u003Cimg src=\"https:\u002F\u002Fwww.deepnote.com\u002Fstatic\u002Fillustration.png\" width=\"150\" \u002F>\u003C\u002Fa>\n\n### Just want to quickly look at some notebooks, without executing any code?\n\nBrowse this repository using [jupyter.org's notebook viewer](https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fageron\u002Fhandson-ml\u002Fblob\u002Fmaster\u002Findex.ipynb):\n\u003Ca href=\"https:\u002F\u002Fnbviewer.jupyter.org\u002Fgithub\u002Fageron\u002Fhandson-ml\u002Fblob\u002Fmaster\u002Findex.ipynb\">\u003Cimg src=\"https:\u002F\u002Fjupyter.org\u002Fassets\u002Flogos\u002Frectanglelogo-greytext-orangebody-greymoons.svg\" width=\"150\" \u002F>\u003C\u002Fa>\n\n_Note_: [github.com's notebook viewer](index.ipynb) also works but it is slower and the math equations are not always displayed correctly.\n\n### Want to run this project using a Docker image?\nRead the [Docker instructions](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml\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-ml.git\n    $ cd handson-ml\n\nNext, run the following commands:\n\n    $ conda env create -f environment.yml\n    $ conda activate tf1\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 3.7. 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 3.8 or 3.9 yet, which is why I recommend Python 3.7.\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\\ 3.7\u002FInstall\\ Certificates.command` in a terminal (change `3.7` 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-ml\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","该项目旨在通过Python教授机器学习的基础知识，包含了《Hands-on Machine Learning with Scikit-Learn and TensorFlow》一书中的示例代码和练习解答。核心功能包括使用Jupyter Notebook进行交互式学习，涵盖了从数据预处理到模型训练的全过程，并利用了Scikit-Learn、TensorFlow等库来实现各种机器学习算法。技术特点上，强调实践操作与理论相结合，适合初学者快速掌握机器学习概念及应用开发技巧。尽管项目已不再更新，但对于希望基于经典工具集（如早期版本的TensorFlow）入门机器学习的学习者来说，仍然是一个非常实用的资源。",2,"2026-06-11 03:23:29","top_topic"]