fastbook README

The project's README from github.com/fastai/fastbook. As one markdown file: README.md.

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The fastai book

These notebooks cover an introduction to deep learning, fastai, and PyTorch. fastai is a layered API for deep learning; for more information, see the fastai paper. Everything in this repo is copyright Jeremy Howard and Sylvain Gugger, 2020 onwards. A selection of chapters is available to read online here.

The notebooks in this repo are used for a MOOC and form the basis of this book, which is currently available for purchase. It does not have the same GPL restrictions that are on this repository.

The 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.

If 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.

Colab

Instead of cloning this repo and opening it on your machine, you can read and work with the notebooks using Google Colab. 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.

You can open any chapter of the book in Colab by clicking on one of these links: Introduction to Jupyter | Chapter 1, Intro | Chapter 2, Production | Chapter 3, Ethics | Chapter 4, MNIST Basics | Chapter 5, Pet Breeds | Chapter 6, Multi-Category | Chapter 7, Sizing and TTA | Chapter 8, Collab | Chapter 9, Tabular | Chapter 10, NLP | Chapter 11, Mid-Level API | Chapter 12, NLP Deep-Dive | Chapter 13, Convolutions | Chapter 14, Resnet | Chapter 15, Arch Details | Chapter 16, Optimizers and Callbacks | Chapter 17, Foundations | Chapter 18, GradCAM | Chapter 19, Learner | Chapter 20, conclusion

Contributions

If 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.)

Citations

If you wish to cite the book, you may use the following:

@book{howard2020deep,
title={Deep Learning for Coders with Fastai and Pytorch: AI Applications Without a PhD},
author={Howard, J. and Gugger, S.},
isbn={9781492045526},
url={https://books.google.no/books?id=xd6LxgEACAAJ},
year={2020},
publisher={O'Reilly Media, Incorporated}
}

fastbook (fastai/fastbook)

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这是什么?本仓库包含《fastai 书》的官方 Jupyter Notebook。这本书是深度学习、fastai 库和 PyTorch 的入门读物。
这是一个编程库还是一本书?这是一本由 Jupyter Notebook 组成的书,不是需要安装的库。同样的笔记本也用于一门 MOOC(大规模在线课程),并构成一个正式出版的纸质书和电子书版本的基础。
我该怎么开始阅读?通过 README 中的链接,直接在 Google Colab 里打开任意章节,无需在本地配置 Python 环境;或者克隆仓库,在自己的电脑上运行笔记本。
学习这本书需要有深度学习的经验吗?不需要。本书是作为教学资料编写的,从图像分类等基础内容讲起,再逐步深入到更高级的网络架构和 fastai 的内部机制,因此面向刚入门这一领域的学习者。
使用这些内容的许可条款是什么?.py 文件和笔记本代码单元格中的代码采用 GPLv3 许可。但所有 Markdown 和文字说明内容都不允许再分发、修改格式或用于商业或广播用途,你只能阅读,或为个人私用而 fork 本仓库。
fastai 是什么?fastai 是一个构建在 PyTorch 之上的分层深度学习库。本书借助它讲解实用的神经网络技术,同时也讲解背后的理论。
有没有可以购买的纸质书或电子书版本?有。这些笔记本是一本单独出版书籍的基础,该书可以购买,且不受本仓库 GPL 限制。
除了英文,这本书还有哪些语言版本?社区翻译版本有西班牙语、韩语、中文、孟加拉语、印尼语、意大利语、葡萄牙语、越南语和日语。
我可以提交对笔记本的修复或修改吗?可以,欢迎提交拉取请求(PR)。但提交后,该贡献的版权将归 Jeremy Howard 和 Sylvain Gugger 所有。
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