[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9578":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":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":25,"hasPages":23,"topics":26,"createdAt":10,"pushedAt":10,"updatedAt":33,"readmeContent":34,"aiSummary":35,"trendingCount":16,"starSnapshotCount":16,"syncStatus":36,"lastSyncTime":37,"discoverSource":38},9578,"fastbook","fastai\u002Ffastbook","fastai","The fastai book, published as Jupyter Notebooks","",null,"Jupyter Notebook",25021,9473,517,116,0,6,19,75,21,45,"Other",false,"master",true,[27,28,29,7,30,31,32],"book","data-science","deep-learning","machine-learning","notebooks","python","2026-06-12 02:02:09","[English](.\u002FREADME.md) \u002F [Spanish](.\u002FREADME_es.md) \u002F [Korean](.\u002FREADME_ko.md) \u002F [Chinese](.\u002FREADME_zh.md) \u002F [Bengali](.\u002FREADME_bn.md) \u002F [Indonesian](.\u002FREADME_id.md) \u002F [Italian](.\u002FREADME_it.md) \u002F [Portuguese](.\u002FREADME_pt.md) \u002F [Vietnamese](.\u002FREADME_vn.md) \u002F [Japanese](.\u002FREADME_ja.md)\n\n# The fastai book\n\nThese notebooks cover an introduction to deep learning, [fastai](https:\u002F\u002Fdocs.fast.ai\u002F), and [PyTorch](https:\u002F\u002Fpytorch.org\u002F). fastai is a layered API for deep learning; for more information, see [the fastai paper](https:\u002F\u002Fwww.mdpi.com\u002F2078-2489\u002F11\u002F2\u002F108). Everything in this repo is copyright Jeremy Howard and Sylvain Gugger, 2020 onwards. A selection of chapters is available to [read online here](https:\u002F\u002Ffastai.github.io\u002Ffastbook2e\u002F).\n\nThe notebooks in this repo are used for [a MOOC](https:\u002F\u002Fcourse.fast.ai) and form the basis of [this book](https:\u002F\u002Fwww.amazon.com\u002FDeep-Learning-Coders-fastai-PyTorch\u002Fdp\u002F1492045527), which is currently available for purchase. It does not have the same GPL restrictions that are on this repository.\n\nThe code in the notebooks and python `.py` files is covered by the GPL v3 license; see the LICENSE file for details. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use. No commercial or broadcast use is allowed. We are making these materials freely available to help you learn deep learning, so please respect our copyright and these restrictions.\n\nIf you see someone hosting a copy of these materials somewhere else, please let them know that their actions are not allowed and may lead to legal action. Moreover, they would be hurting the community because we're not likely to release additional materials in this way if people ignore our copyright.\n\n## Colab\n\nInstead of cloning this repo and opening it on your machine, you can read and work with the notebooks using [Google Colab](https:\u002F\u002Fresearch.google.com\u002Fcolaboratory\u002F). This is the recommended approach for folks who are just getting started -- there's no need to set up a Python development environment on your own machine, since you can just work directly in your web-browser.\n\nYou can open any chapter of the book in Colab by clicking on one of these links: [Introduction to Jupyter](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002Fapp_jupyter.ipynb) | [Chapter 1, Intro](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F01_intro.ipynb) | [Chapter 2, Production](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F02_production.ipynb) | [Chapter 3, Ethics](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F03_ethics.ipynb) | [Chapter 4, MNIST Basics](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F04_mnist_basics.ipynb) | [Chapter 5, Pet Breeds](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F05_pet_breeds.ipynb) | [Chapter 6, Multi-Category](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F06_multicat.ipynb) | [Chapter 7, Sizing and TTA](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F07_sizing_and_tta.ipynb) | [Chapter 8, Collab](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F08_collab.ipynb) | [Chapter 9, Tabular](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F09_tabular.ipynb) | [Chapter 10, NLP](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F10_nlp.ipynb) | [Chapter 11, Mid-Level API](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F11_midlevel_data.ipynb) | [Chapter 12, NLP Deep-Dive](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F12_nlp_dive.ipynb) | [Chapter 13, Convolutions](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F13_convolutions.ipynb) | [Chapter 14, Resnet](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F14_resnet.ipynb) | [Chapter 15, Arch Details](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F15_arch_details.ipynb) | [Chapter 16, Optimizers and Callbacks](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F16_accel_sgd.ipynb) | [Chapter 17, Foundations](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F17_foundations.ipynb) | [Chapter 18, GradCAM](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F18_CAM.ipynb) | [Chapter 19, Learner](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F19_learner.ipynb) | [Chapter 20, conclusion](https:\u002F\u002Fcolab.research.google.com\u002Fgithub\u002Ffastai\u002Ffastbook\u002Fblob\u002Fmaster\u002F20_conclusion.ipynb)\n\n\n## Contributions\n\nIf you make any pull requests to this repo, then you are assigning copyright of that work to Jeremy Howard and Sylvain Gugger. (Additionally, if you are making small edits to spelling or text, please specify the name of the file and a very brief description of what you're fixing. It's difficult for reviewers to know which corrections have already been made. Thank you.)\n\n## Citations\n\nIf you wish to cite the book, you may use the following:\n\n```\n@book{howard2020deep,\ntitle={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD},\nauthor={Howard, J. and Gugger, S.},\nisbn={9781492045526},\nurl={https:\u002F\u002Fbooks.google.no\u002Fbooks?id=xd6LxgEACAAJ},\nyear={2020},\npublisher={O'Reilly Media, Incorporated}\n}\n```\n\n","fastai\u002Ffastbook项目是基于Jupyter Notebook编写的深度学习入门书籍，涵盖了fastai库和PyTorch框架的使用。其核心功能在于通过一系列实践性强的代码示例与解释来教授读者如何利用这些工具进行深度学习模型的设计与训练。技术特点包括以交互式笔记的形式呈现内容，便于学习者边学边练，并且支持多种语言版本，提高了全球范围内的可访问性。该项目非常适合希望快速掌握深度学习基础知识以及熟悉fastai API的学生、开发者或研究人员，在线提供的Google Colab链接使得无需本地安装环境即可开始学习成为可能。",2,"2026-06-11 03:23:33","top_topic"]