machinelearning_notebook/References.md

# References
可以自行在下属列表找找到适合自己的学习资料，虽然罗列的比较多，但是个人最好选择一个深入阅读、练习。当练习到一定程度，可以再看看其他的资料，这样弥补单一学习资料可能存在的欠缺。


## Python & IPython
* [Python教程](https://www.liaoxuefeng.com/wiki/0014316089557264a6b348958f449949df42a6d3a2e542c000)
* [Python-Lectures](https://github.com/rajathkmp/Python-Lectures)
* [A gallery of interesting Jupyter Notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks)
* [IPython tutorials](https://nbviewer.jupyter.org/github/ipython/ipython/blob/master/examples/IPython%20Kernel/Index.ipynb)
* [Examples from the IPython mini-book](https://github.com/rossant/ipython-minibook)
* [Code of the IPython Cookbook, Second Edition (2018)](https://github.com/ipython-books/cookbook-2nd-code)


## Libs
* [numpy](http://www.numpy.org/)
* [matplotlib - 2D and 3D plotting in Python](http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/blob/master/Lecture-4-Matplotlib.ipynb)
* [scipy](https://www.scipy.org/)
* [pytorch](https://pytorch.org/)
* [tensorflow](https://www.tensorflow.org/)


## Machine learning

* [ipython-notebooks: A collection of IPython notebooks covering various topics](https://github.com/jdwittenauer/ipython-notebooks)
* [Learn Data Science](http://learnds.com/)
* [AM207 2016](https://github.com/AM207/2016/tree/master)
* [Python机器学习](https://ljalphabeta.gitbooks.io/python-/content/)
* [scientific-python-lectures](http://nbviewer.jupyter.org/github/jrjohansson/scientific-python-lectures/tree/master/)


## Awesome series
* [Awesome Cmputer Vision](https://github.com/jbhuang0604/awesome-computer-vision)
* [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)
* [Awesome - Most Cited Deep Learning Papers](https://github.com/terryum/awesome-deep-learning-papers)
* [Awesome Deep Vision](https://github.com/kjw0612/awesome-deep-vision)
* [Awesome 3D Reconstruction](https://github.com/openMVG/awesome_3DReconstruction_list)

## Lectures
* [Machine Learning](https://www.coursera.org/learn/machine-learning)
* [CS229: Machine Learning](http://cs229.stanford.edu/)
* [CS 20: Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/index.html)
* [CS 294: Deep Reinforcement Learning, UC Berkeley](http://rll.berkeley.edu/deeprlcourse/)
* [Deep Learning Book](https://github.com/exacity/deeplearningbook-chinese)
* [Machine Learning Crash Course with TensorFlow APIs](https://developers.google.cn/machine-learning/crash-course/)
* [ Nvidia DLI](https://www.nvidia.com/zh-cn/deep-learning-ai/education/)
* [Introduction to Machine Learning](https://webdocs.cs.ualberta.ca/~nray1/CMPUT466_551.htm)
* [Computer Vision @ ETHZ](http://cvg.ethz.ch/teaching/compvis/)
* [SFMedu: A Structure from Motion System for Education](http://robots.princeton.edu/courses/SFMedu/)
* [Scene understanding of computer vision](http://vision.princeton.edu/courses/COS598/2014sp/)
* [Autonomous Navigation for Flying Robots](http://vision.in.tum.de/teaching/ss2015/autonavx)
* [Multiple View Geometry](http://vision.in.tum.de/teaching/ss2015/mvg2015)
* [Deep Learning for Self-Driving Cars](https://selfdrivingcars.mit.edu/)
* [史上最全TensorFlow学习资源汇总](https://www.toutiao.com/a6543679835670053380/)
* [Oxford Deep NLP 2017 course](https://github.com/oxford-cs-deepnlp-2017/lectures)