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1 | 1 | # some books and slides about PyTorch |
2 | 2 |
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3 | | -> Note: some of these are old version |
| 3 | +> Note: some of these are old version; 下面的书籍部分还不是1.0版本。。毕竟1.0刚出所以书籍较少。。 |
| 4 | + |
| 5 | +* A brief summary of the PTDC ’18 PyTorch 1.0 Preview and Promise - Hacker Noon.pdf |
| 6 | +* Automatic differentiation in PyTorch.pdf |
| 7 | +* Deep Architectures.pdf |
| 8 | +* Deep Architectures.pptx |
| 9 | +* Deep Learning Toolkits II pytorch example.pdf |
| 10 | +* Deep Learning with PyTorch - Vishnu Subramanian.pdf |
| 11 | +* Deep_Learning_with_PyTorch_Quick_Start_Guide.pdf |
| 12 | +* pytorch 0.4 - tutorial - 有目录版.pdf |
| 13 | +* PyTorch 0.4 中文文档 - 翻译.pdf |
| 14 | +* PyTorch 1.0 Bringing research and production together Presentation.pdf |
| 15 | +* PyTorch Recipes - A Problem-Solution Approach - Pradeepta Mishra.pdf |
| 16 | +* PyTorch under the hood A guide to understand PyTorch internals.pdf |
| 17 | +* PyTorch_tutorial_0.0.4_余霆嵩.pdf |
| 18 | +* PyTorch_tutorial_0.0.5_余霆嵩.pdf |
| 19 | +* pytorch-internals.pdf |
| 20 | +* pytorch卷积、反卷积 - download from internet.pdf |
| 21 | +* PyTorch深度学习实战 - 侯宜军.epub |
| 22 | +* PyTorch深度学习实战 - 侯宜军.pdf |
| 23 | +* 深度学习框架PyTorch:入门与实践 - 陈云.pdf |
| 24 | +* 深度学习入门之PyTorch - 廖星宇(有目录).pdf |
| 25 | +* 深度学习之Pytorch - 廖星宇.pdf |
| 26 | +* 深度学习之PyTorch实战计算机视觉 - 唐进民.pdf |
| 27 | + |
| 28 | + |
4 | 29 |
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5 | | -* Deep Learning Toolkits II pytorch example |
6 | | -* PyTorch 1.0 Bringing research and production together Presentation |
7 | | -* Deep Learning with PyTorch - Packet Vishnu Subramanian |
8 | | -* PyTorch深度学习实战 - 侯宜军(pdf) |
9 | | -* PyTorch深度学习实战 - 侯宜军(epub) |
10 | | -* 深度学习之Pytorch - 廖星宇 |
11 | | -* 深度学习之PyTorch实战计算机视觉 - 唐进民 |
12 | | -* pytorch卷积、反卷积 - download from internet |
13 | | -* A brief summary of the PTDC ’18 PyTorch 1.0 Preview and Promise - Hacker Noon |
14 | | -* PyTorch_tutorial_0.0.4_余霆嵩 |
15 | | -* 深度学习入门之PyTorch - 廖星宇(有目录) |
16 | | -* 深度学习框架PyTorch:入门与实践 - 陈云 |
17 | | -* PyTorch 0.4 中文文档 - 翻译 |
18 | | -* pytorch 0.4 - tutorial - 有目录版 |
19 | | -* Automatic differentiation in PyTorch - paper |
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