You can not select more than 25 topics Topics must start with a chinese character,a letter or number, can include dashes ('-') and can be up to 35 characters long.

README.md 2.0 kB

3 years ago
3 years ago
1234567891011121314151617181920212223242526272829303132333435
  1. # PyTorch
  2. PyTorch是基于Python的科学计算包,其旨在服务两类场合:
  3. * 替代NumPy发挥GPU潜能
  4. * 提供了高度灵活性和效率的深度学习平台
  5. PyTorch的简洁设计使得它入门很简单,本部分内容在深入介绍PyTorch之前,先介绍一些PyTorch的基础知识,让大家能够对PyTorch有一个大致的了解,并能够用PyTorch搭建一个简单的神经网络,然后在深入学习如何使用PyTorch实现各类网络结构。在学习过程,可能部分内容暂时不太理解,可先不予以深究,后续的课程将会对此进行深入讲解。
  6. ![PyTorch Demo](imgs/PyTorch.png)
  7. ## 内容
  8. - [Tensor](1-tensor.ipynb)
  9. - [autograd](2-autograd.ipynb)
  10. - [linear-regression](3-linear-regression.ipynb)
  11. - [logistic-regression](4-logistic-regression.ipynb)
  12. - [nn-sequential-module](5-nn-sequential-module.ipynb)
  13. - [deep-nn](6-deep-nn.ipynb)
  14. - [param_initialize](7-param_initialize.ipynb)
  15. - [optim/sgd](optimizer/6_1-sgd.ipynb)
  16. - [optim/adam](optimizer/6_6-adam.ipynb)
  17. ## References
  18. * [code of book "Learn Deep Learning with PyTorch"](https://github.com/L1aoXingyu/code-of-learn-deep-learning-with-pytorch)
  19. * [PyTorch tutorials and fun projects including neural talk, neural style, poem writing, anime generation](https://github.com/chenyuntc/pytorch-book)
  20. * [Awesome-Pytorch-list](https://github.com/bharathgs/Awesome-pytorch-list)
  21. * [PyTorch Tutorial for Deep Learning Researchers](https://github.com/yunjey/pytorch-tutorial)
  22. * [The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.](https://github.com/ritchieng/the-incredible-pytorch)
  23. * [Simple examples to introduce PyTorch](https://github.com/jcjohnson/pytorch-examples)
  24. * [Simple PyTorch Tutorials Zero to ALL!](https://github.com/hunkim/PyTorchZeroToAll)
  25. * [从基础概念到实现,小白如何快速入门PyTorch](https://mp.weixin.qq.com/s/zhkaenFdnB5KgaEYb-XDEQ)

机器学习越来越多应用到飞行器、机器人等领域,其目的是利用计算机实现类似人类的智能,从而实现装备的智能化与无人化。本课程旨在引导学生掌握机器学习的基本知识、典型方法与技术,通过具体的应用案例激发学生对该学科的兴趣,鼓励学生能够从人工智能的角度来分析、解决飞行器、机器人所面临的问题和挑战。本课程主要内容包括Python编程基础,机器学习模型,无监督学习、监督学习、深度学习基础知识与实现,并学习如何利用机器学习解决实际问题,从而全面提升自我的《综合能力》。