├── README.md ├── bigdata.md ├── cv.md ├── dl.md ├── gis.md ├── math.md ├── ml.md ├── nlp.md └── rl.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome 2 | [![ODS stickers](https://github.com/Yorko/mlcourse_open/blob/master/img/ods_stickers.jpg)](http://ods.ai) 3 | Cherry picked DS educational materials 4 | -------------------------------------------------------------------------------- /bigdata.md: -------------------------------------------------------------------------------- 1 | # [Big data](https://opendatascience.slack.com/messages/big_data/) 2 | 3 | - [Hadoop. Система для обработки больших объемов данных](https://stepik.org/course/Hadoop-%D0%A1%D0%B8%D1%81%D1%82%D0%B5%D0%BC%D0%B0-%D0%B4%D0%BB%D1%8F-%D0%BE%D0%B1%D1%80%D0%B0%D0%B1%D0%BE%D1%82%D0%BA%D0%B8-%D0%B1%D0%BE%D0%BB%D1%8C%D1%88%D0%B8%D1%85-%D0%BE%D0%B1%D1%8A%D0%B5%D0%BC%D0%BE%D0%B2-%D0%B4%D0%B0%D0%BD%D0%BD%D1%8B%D1%85-150) 4 | -------------------------------------------------------------------------------- /cv.md: -------------------------------------------------------------------------------- 1 | # Computer Vision (not [deep learning](./dl.md)) 2 | 3 | - [курсы Антона Конушина](https://www.youtube.com/user/aktoshik/playlists) 4 | - [лекции Натальи Васильевой](https://www.lektorium.tv/course/22902) 5 | - [COS429 Fall 2014: Computer Vision](http://vision.princeton.edu/courses/COS429/2014fa/) @ Princeton 6 | - [Image Analysis Class (lectures 2013/2015) ](https://www.youtube.com/playlist?list=PLuRaSnb3n4kSgSV35vTPDRBH81YgnF3Dd) @ Universität Heidelberg 7 | -------------------------------------------------------------------------------- /dl.md: -------------------------------------------------------------------------------- 1 | # Deep Learning 2 | 3 | - CS231n: Convolutional Neural Networks for Visual Recognition. [video](https://www.youtube.com/playlist?list=PLkt2uSq6rBVctENoVBg1TpCC7OQi31AlC), [github](http://cs231n.github.io/) @ Stanford 4 | - [IFT6266 H-2016 Deep Learning](https://ift6266h16.wordpress.com/category/lectures/) @ U. Montreal 5 | - [Yandex SDA deeplearning 17](https://github.com/yandexdataschool/YSDA_deeplearning17) @ Yandex SDA 6 | - [CSC 2541 Fall 2016: 7 | Differentiable Inference and Generative Models](http://www.cs.toronto.edu/~duvenaud/courses/csc2541/index.html) @ University of Toronto 8 | - [CS 20SI: Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/syllabus.html) @ Stanford 9 | 10 | 11 | ## We need to go deeper 12 | - [A curated list of deep learning resources for computer vision](https://github.com/kjw0612/awesome-deep-vision) 13 | - [A curated list of awesome Deep Learning tutorials, projects and communities. ](https://github.com/ChristosChristofidis/awesome-deep-learning) 14 | -------------------------------------------------------------------------------- /gis.md: -------------------------------------------------------------------------------- 1 | # GIS 2 | 3 | - [Introduction to PostGIS](http://workshops.boundlessgeo.com/postgis-intro/) 4 | - [A Gentle Introduction to GIS](https://docs.qgis.org/2.8/en/docs/gentle_gis_introduction/) 5 | - [Настройка OSM-сервера с нуля](https://docs.google.com/document/d/1PEihuj0P1lN3zJZX6OLohRYnkzQxrdju1t-S_M2Zyo0/edit) 6 | -------------------------------------------------------------------------------- /math.md: -------------------------------------------------------------------------------- 1 | # Mathematics 2 | 3 | ## Probability 4 | - [Introduction to Probability - The Science of Uncertainty](https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2) @ MIT 5 | - [Statistics 110: Probability](http://projects.iq.harvard.edu/stat110) @ Harvard 6 | - [Теория вероятностей для начинающих](https://ru.coursera.org/learn/probability-theory-basics) @ MIPT 7 | - [probability cheatsheet 8 | ](https://github.com/wzchen/probability_cheatsheet/blob/master/probability_cheatsheet.pdf) 9 | 10 | ## Linear algebra 11 | - [Essence of linear algebra](https://www.youtube.com/watch?v=kjBOesZCoqc&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) 12 | - [Linear Algebra by Strang G.](http://www-math.mit.edu/~gs/) @ MIT 13 | - [Linear Algebra Through Computer Science Applications](http://codingthematrix.com/) 14 | - [Matrix Cookbook](https://archive.org/details/imm3274) 15 | -------------------------------------------------------------------------------- /ml.md: -------------------------------------------------------------------------------- 1 | # Machine Learning 2 | 3 | - [Ng](https://www.youtube.com/playlist?list=PLJ1-ciQ35nuiyL1PX6O4NdF5CjjaDdnVC) @ Stanford 4 | - [CS229 - Machine Learning](https://see.stanford.edu/Course/CS229) @ Stanford 5 | -------------------------------------------------------------------------------- /nlp.md: -------------------------------------------------------------------------------- 1 | # [NLP](https://opendatascience.slack.com/messages/nlp/) 2 | 3 | - [CS224d: Deep Learning for Natural Language Processing](http://cs224d.stanford.edu/syllabus.html) @ Stanford 4 | - [Oxford Deep NLP 2017 course](https://github.com/oxford-cs-deepnlp-2017) @ Oxford 5 | -------------------------------------------------------------------------------- /rl.md: -------------------------------------------------------------------------------- 1 | # Reinforcement Learning 2 | 3 | - [RL Course by David Silver ](https://www.youtube.com/watch?v=2pWv7GOvuf0&list=PLzuuYNsE1EZAXYR4FJ75jcJseBmo4KQ9-) @ DeepMind 4 | - [A course on reinforcement learning in the wild](https://github.com/yandexdataschool/Practical_RL/) @ HSE and Yandex SDA 5 | - [CS 294: Deep Reinforcement Learning, Spring 2017](http://rll.berkeley.edu/deeprlcourse/) @ Berkley 6 | --------------------------------------------------------------------------------