Original article can be found here (source): Deep Learning on Medium
Although the book is currently in pre-order status, it is highly anticipated by readers and has long been ranked the first on Amazon’s Computer Science book list.
The draft of the book has published 22 chapters (including introduction and conclusion). The content starts with the most well known AI “Hello Word problem”, the MNIST image classification, then NLP, recurrent neural network, convolutional neural network, and interpretability.
This course is not for beginner and the prerequisites are knowledge of Python and PyTorch.
To run the code in Jupyter Notebook, you need to install:
fastai v2、Graphviz、ipywidgets、matplotlib、nbdev、pandas、scikit-learn、Microsoft Azure Cognitive Services Image Search
They can all be installed directly via PyPI.
This fastbook is not only a textbook, but also an AI community resource. In the final message, the author hopes that everyone who has completed this book will exchange successful experiences with everyone.
Finally, it is very important to mention copyright.
Everything in the fastbook project is copyright Jeremy Howard and Sylvain Gugger, 2020 onwards.
The code in the notebooks and python
.py files is covered by the GPL v3 license. 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.
And here is the message from Jeremy Howard in regarding to open source and copyright
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.
Jeremy Howard, from fastbook readme
Here are links again