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Laundry List of Data Science / ML /AI resources available online
https://github.com/sayantansatpati/awesome-data-science

List: awesome-data-science

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Laundry List of Data Science / ML /AI resources available online

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README 

        
# Data Science Resources

*A curated list of Data Science resources available online*



### Table of Contents

- **[Video Lectures](#video-lectures)**  

    - [ML and DL MOOCs](#ml-and-dl-moocs)

    - [Mathematics](#mathematics)

    - [Other Online Video Lectures](#other-online-video-lectures)

- **[Reading Materials](#reading-materials)**

    - [Research Papers](#research-papers)

    - [GitHub](#github)

    - [Blogs](#blogs)

    - [Online Books](#online-books)

    - [Notes](#notes)

    - [Cheat Sheets](#cheat-sheets)

    - [Misc Others](#misc-others)

- **[Articles](#articles)**

    - [Forward and Backpropagation](#forward-and-backpropagation)

    - [RNN](#rnn)

    - [Collection of Useful Resources](#collection-of-useful-resources)

    - [Quora](#quora)

    - [Misc](#misc)



## Video Lectures



### ML and DL MOOCs



1. [Coursera: Andrew NG](https://www.coursera.org/learn/machine-learning)

2. [Coursera: Geoffrey Hinton](https://www.coursera.org/learn/neural-networks)

3. [Caltech: Abu Mostafa](http://work.caltech.edu/telecourse.html)

4. [Stanford: NLP cs224n](http://web.stanford.edu/class/cs224n/), [Coursera Videos](https://www.youtube.com/watch?v=nfoudtpBV68&index=1&list=PL6397E4B26D00A269)

5. [Stanford: CNN cs231n](http://cs231n.github.io/), [Videos](https://archive.org/details/cs231n-CNNs), [Spring 2017](https://www.youtube.com/playlist?list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv&app=desktop)

6. [Stanford: MMDS](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+MMDS+SelfPaced/about)

7. [Berkeley: Deep Reinforcement Learning](http://rll.berkeley.edu/deeprlcourse/)

8. [Stanford: Convex Optimization](http://online.stanford.edu/course/convex-optimization-winter-2014)

9. [Stanford: Probabilistic Graphical Models](https://www.coursera.org/specializations/probabilistic-graphical-models)

10. [Big Data University: TensorFlow](https://bigdatauniversity.com/courses/deep-learning-tensorflow/)

11. [Kadenze: TensorFlow](https://www.kadenze.com/courses/creative-applications-of-deep-learning-with-tensorflow-iv/info)

12. [Udacity Nanodegree: AI](https://www.udacity.com/ai)

13. [Udacity Nanodegree: Self Driving Car](https://www.udacity.com/drive)

14. [EDX: Berkeley X-Series Spark](https://www.edx.org/xseries/data-science-engineering-apacher-sparktm#courses)

15. [Oxford Deep NLP: 2017 SLides Only](https://github.com/oxford-cs-deepnlp-2017/lectures)



### Mathematics



1. [Khan Academy: Linear Algebra](https://www.khanacademy.org/math/linear-algebra)

2. [Khan Academy: Calculus](https://www.khanacademy.org/math/calculus-home)

3. [MIT: Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/)

4. [MIT: Calculus](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/)

5. [Harvard: Stat110](http://projects.iq.harvard.edu/stat110/youtube)



### Other Online Video Lectures



1. [Stanford: cs231n Winter 2016 lectures](https://www.youtube.com/playlist?list=PLLvH2FwAQhnpj1WEB-jHmPuUeQ8mX-XXG)

2. [Bay Area Deep Learning School](http://www.bayareadlschool.org/)

  * [1st Day](https://www.youtube.com/watch?v=eyovmAtoUx0)

  * [2nd Day](https://www.youtube.com/watch?v=9dXiAecyJrY)

3. [Deep Learning Summer School, Montreal](http://videolectures.net/deeplearning2016_montreal/)

4. [TensorFlow Without a PhD](https://cloud.google.com/blog/big-data/2017/01/learn-tensorflow-and-deep-learning-without-a-phd)



## Reading Materials

 

### Research Papers



1. [arxiv](https://arxiv.org/)

2. [gitxiv](http://www.gitxiv.com/)

3. [Deep Learning Reading List](http://deeplearning.net/reading-list/)

4. [Hacker Dojo Deep Learning Study Papers](https://github.com/mike-bowles/hdDeepLearningStudy)

5. [Deep Learning Papers Reading Roadmap](https://github.com/songrotek/Deep-Learning-Papers-Reading-Roadmap)

6. [Awesome Deep Vision](https://github.com/kjw0612/awesome-deep-vision)

7. [The-9-Deep-Learning-Papers-You-Need-To-Know-About](https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-You-Need-To-Know-About.html)

8. [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers)



### Collection of Resources

1. [Arthur Chan's Top-5 DL](http://thegrandjanitor.com/2016/08/15/learning-deep-learning-my-top-five-resource/)



### GitHub

1. [top-10-data-science-github](http://www.kdnuggets.com/2016/03/top-10-data-science-github.html)

2. [Awesome Data Science](https://github.com/bulutyazilim/awesome-datascience)

3. [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning)

4. [Awesome Deep Learning](https://github.com/ChristosChristofidis/awesome-deep-learning)

5. [Awesome Deep Vision](https://github.com/kjw0612/awesome-deep-vision)

6. [Awesome Artifical Intelligence](https://github.com/owainlewis/awesome-artificial-intelligence)

7. [awesome-deep-learning-papers](https://github.com/terryum/awesome-deep-learning-papers)

8. [A-gallery-of-interesting-Jupyter-and-IPython-Notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-and-IPython-Notebooks)

9. [Collection of Data Science iPython Notebooks](https://github.com/donnemartin/data-science-ipython-notebooks)

10. [Stanford: cs231n](http://cs231n.github.io/)

11. [Adrej Karpathy](http://karpathy.github.io/)

12. [Hvass-Labs: TensorFlow Tutorial Notebooks](https://github.com/Hvass-Labs/TensorFlow-Tutorials)

13. [machine-learning-for-software-engineers](https://github.com/ZuzooVn/machine-learning-for-software-engineers)

14. [data-scientists-to-follow-best-tutorials](https://www.analyticsvidhya.com/blog/2015/07/github-special-data-scientists-to-follow-best-tutorials/)

15. [Oxford Deep NLP: 2017](https://github.com/oxford-cs-deepnlp-2017/lectures)



### Blogs

1. [colah's blog](http://colah.github.io/)

2. [iamtrask](https://iamtrask.github.io/)

3. [Medium](https://medium.com/)

4. [KDNuggets](http://www.kdnuggets.com/)

5. [datasciencecentral](http://www.datasciencecentral.com/)

6. [machinelearningmastery](http://machinelearningmastery.com/blog/)

7. [Edwin Chen's blog](http://blog.echen.me/)

8. [Hunch](http://hunch.net/)

9. [frequently-updated-machine-learning-blogs](http://bigdata-madesimple.com/frequently-updated-machine-learning-blogs/)

10. [Arthur Chan's Blog](http://thegrandjanitor.com/)



### Online Books

1. [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/index.html)

2. [Deep Learning Book](http://www.deeplearningbook.org/)

3. [An Introduction to Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/)

4. [The Elements of Statistical Learning](https://statweb.stanford.edu/~tibs/ElemStatLearn/)

5. [A Course in Machine Learning](http://ciml.info/)

    

### Notes

1. [cs229: ML Course Materials](http://cs229.stanford.edu/materials.html)



### Cheat Sheets

1. [KDNuggets: data-science-machine-learning-cheat-sheets](http://www.kdnuggets.com/2016/12/data-science-machine-learning-cheat-sheets-updated.html)



### Misc Others

1. [TensorFlow](https://www.tensorflow.org/tutorials/)

2. [Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/syllabus.html)

3. [TensorFlow-Examples](https://github.com/aymericdamien/TensorFlow-Examples)

4. [pytorch-tutorial](https://github.com/yunjey/pytorch-tutorial/blob/master/README.md)

5. [learning-deep-learning-my-top-five-resource](http://thegrandjanitor.com/2016/08/15/learning-deep-learning-my-top-five-resource/)



## Articles



### Forward and Backpropagation



1. [yes-you-should-understand-backprop](https://medium.com/@karpathy/yes-you-should-understand-backprop-e2f06eab496b#.6ipdwn1is)

2. [Neural Network in Python](http://iamtrask.github.io/2015/07/12/basic-python-network/)

3. [Step By Step Backpropagation](https://mattmazur.com/2015/03/17/a-step-by-step-backpropagation-example/)



### RNN

1. [Andrej Karpathy's lecture](https://www.youtube.com/watch?v=iX5V1WpxxkY)

2. [Christopher Olah on how LSTMs work](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

3. [RNN using TensorFlow](http://r2rt.com/recurrent-neural-networks-in-tensorflow-i.html)

4. [Andrej Karpathy's Blog](http://karpathy.github.io/2015/05/21/rnn-effectiveness/)



### Useful Resources



1. [Open Source Data Science Masters](http://datasciencemasters.org/)

2. [analyticsvidhya: 21 Deep Learning Videos (2016)](https://www.analyticsvidhya.com/blog/2016/12/21-deep-learning-videos-tutorials-courses-on-youtube-from-2016/)

3. [analyticsvidhya: Top YouTube Videos (2015)](https://www.analyticsvidhya.com/blog/2015/07/top-youtube-videos-machine-learning-neural-network-deep-learning/)

4. [Free Python Books](http://pythonbooks.revolunet.com/)

5. [16 Free Machine Learning Books](https://hackerlists.com/free-machine-learning-books/)

6. [frequently-updated-machine-learning-blogs](http://bigdata-madesimple.com/frequently-updated-machine-learning-blogs/)



### Quora



1. [What-are-the-best-machine-learning-blogs-or-resources-available](https://www.quora.com/What-are-the-best-machine-learning-blogs-or-resources-available)

2. [How-do-I-learn-machine-learning-1](https://www.quora.com/How-do-I-learn-machine-learning-1)

3. [What-are-some-good-books-papers-for-learning-deep-learning](https://www.quora.com/What-are-some-good-books-papers-for-learning-deep-learning)



### Misc



1. [Visual Information Theory](https://colah.github.io/posts/2015-09-Visual-Information/)

2. [4 Steps for Learning Deep Learning](https://medium.com/@vzkuma/4-steps-for-learning-deep-learning-86f11fcee54#.gnfxqsk54)

3. [How to build a Recurrent Neural Network in TensorFlow (1/7)](https://medium.com/@erikhallstrm/hello-world-rnn-83cd7105b767#.rj930jywc)

4. [Understanding-LSTMs](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)

5. [anyone-can-code-lstm](https://iamtrask.github.io/2015/11/15/anyone-can-code-lstm/)