> For the complete documentation index, see [llms.txt](https://ykkim.gitbook.io/dlip/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ykkim.gitbook.io/dlip/deep-learning-for-perception/resource.md).
# Resource
## Programming Tutorial:
#### [Over 200 of the Best Machine Learning, NLP, and Python Tutorials](https://medium.com/machine-learning-in-practice/over-200-of-the-best-machine-learning-nlp-and-python-tutorials-2018-edition-dd8cf53cb7dc)
### Python
#### Recommended
[A Crash Course in Python for Scientists](http://nbviewer.jupyter.org/gist/rpmuller/5920182#ii.-numpy-and-scipy) (nbviewer.jupyter.org)
[Python Numpy Tutorial](http://cs231n.github.io/python-numpy-tutorial/) (Stanford CS231n)
#### Others
[Learn X in Y Minutes (X = Python)](http://learnxinyminutes.com/docs/python/) (brief course for 30 min)
[Google Developers Python Course](https://www.youtube.com/playlist?list=PLfZeRfzhgQzTMgwFVezQbnpc1ck0I6CQl) (highly recommended for visual learners)
[Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning#python) (github.com/josephmisiti)
[7 Steps to Mastering Machine Learning With Python](http://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html) (kdnuggets.com)
[An example machine learning notebook](http://nbviewer.jupyter.org/github/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb) (nbviewer.jupyter.org)
[Machine Learning with Python](https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_quick_guide.htm) (tutorialspoint.com)
###
### Scipy and numpy
#### Recommended
[Python Numpy Tutorial](http://cs231n.github.io/python-numpy-tutorial/) (Stanford CS231n)
#### Others
[Scipy Lecture Notes](http://www.scipy-lectures.org/) (scipy-lectures.org)
[Python Numpy Tutorial](http://cs231n.github.io/python-numpy-tutorial/) (Stanford CS231n)
[An introduction to Numpy and Scipy](https://engineering.ucsb.edu/~shell/che210d/numpy.pdf) (UCSB CHE210D)
[A Crash Course in Python for Scientists](http://nbviewer.jupyter.org/gist/rpmuller/5920182#ii.-numpy-and-scipy) (nbviewer.jupyter.org)
### PyTorch
#### Recommended
[PyTorch Tutorials](http://pytorch.org/tutorials/) (pytorch.org)
#### Others
[A Gentle Intro to PyTorch](http://blog.gaurav.im/2017/04/24/a-gentle-intro-to-pytorch/) (gaurav.im)
[Tutorial: Deep Learning in PyTorch](https://iamtrask.github.io/2017/01/15/pytorch-tutorial/) (iamtrask.github.io)
[PyTorch Examples](https://github.com/jcjohnson/pytorch-examples) (github.com/jcjohnson)
[PyTorch Tutorial](https://github.com/MorvanZhou/PyTorch-Tutorial) (github.com/MorvanZhou)
[PyTorch Tutorial for Deep Learning Researchers](https://github.com/yunjey/pytorch-tutorial) (github.com/yunjey)
###
### Tensorflow
#### Recommended
[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/) (google.com)
[Tensorflow Tutorials](https://www.tensorflow.org/tutorials/) (tensorflow\.org)
#### Others
[Introduction to TensorFlow — CPU vs GPU](https://medium.com/@erikhallstrm/hello-world-tensorflow-649b15aed18c) (medium.com/@erikhallstrm)
[TensorFlow: A primer](https://blog.metaflow.fr/tensorflow-a-primer-4b3fa0978be3) (metaflow\.fr)
[RNNs in Tensorflow](http://www.wildml.com/2016/08/rnns-in-tensorflow-a-practical-guide-and-undocumented-features/) (wildml.com)
[Implementing a CNN for Text Classification in TensorFlow](http://www.wildml.com/2015/12/implementing-a-cnn-for-text-classification-in-tensorflow/) (wildml.com)
[How to Run Text Summarization with TensorFlow](http://pavel.surmenok.com/2016/10/15/how-to-run-text-summarization-with-tensorflow/) (surmenok.com)
[Tutorial of Tensorflow with Youtube](https://github.com/Hvass-Labs/TensorFlow-Tutorials)
###
###
## Math
[Math for Machine Learning](https://people.ucsc.edu/~praman1/static/pub/math-for-ml.pdf) (ucsc.edu)
[Math for Machine Learning](http://www.umiacs.umd.edu/~hal/courses/2013S_ML/math4ml.pdf) (UMIACS CMSC422)
### Probability
[Understanding Bayes Theorem With Ratios](https://betterexplained.com/articles/understanding-bayes-theorem-with-ratios/) (betterexplained.com)
[Review of Probability Theory](http://cs229.stanford.edu/section/cs229-prob.pdf) (Stanford CS229)
[Probability Theory Review for Machine Learning](https://see.stanford.edu/materials/aimlcs229/cs229-prob.pdf) (Stanford CS229)
[Probability Theory](http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/Probability-Theory.pdf) (U. of Buffalo CSE574)
[Probability Theory for Machine Learning](http://www.cs.toronto.edu/~urtasun/courses/CSC411_Fall16/tutorial1.pdf) (U. of Toronto CSC411)
### Linear algebra
[An Intuitive Guide to Linear Algebra](https://betterexplained.com/articles/linear-algebra-guide/) (betterexplained.com)
[A Programmer’s Intuition for Matrix Multiplication](https://betterexplained.com/articles/matrix-multiplication/) (betterexplained.com)
[Understanding the Cross Product](https://betterexplained.com/articles/cross-product/) (betterexplained.com)
[Understanding the Dot Product](https://betterexplained.com/articles/vector-calculus-understanding-the-dot-product/) (betterexplained.com)
[Linear Algebra for Machine Learning](http://www.cedar.buffalo.edu/~srihari/CSE574/Chap1/LinearAlgebra.pdf) (U. of Buffalo CSE574)
[Linear algebra cheat sheet for deep learning](https://medium.com/towards-data-science/linear-algebra-cheat-sheet-for-deep-learning-cd67aba4526c) (medium.com)
[Linear Algebra Review and Reference](http://cs229.stanford.edu/section/cs229-linalg.pdf) (Stanford CS229)