Various References

Important

This is an old homepage used by me from 2011 to 2020. The new site is under construction.

Federated Learning

1. https://inst.eecs.berkeley.edu/~cs294-163/fa19/slides/federated-learning.pdf

2. https://inst.eecs.berkeley.edu/~cs294-163/fa19/

Time

• 1. USA, Palo-Alto

  2. Russia, Moscow

Parallel Compute

1. CME 213 Introduction to parallel computing using MPI, openMP, and CUDA http://ericdarve.github.io/

2. CME 194: Introduction to MPI http://web.stanford.edu/class/cme194/cgi-bin/

Blog of companies and people

1. Блог компании Яндекс

2. Song Ho Ahn. Небольшие, но классные статьи по интересным около программистким вещам 

3. Jim. Нет его контактных данных и т.д. На одной из страниц он делал описание D-way heap (с ошибками, но всё же)

4. https://www3.ntu.edu.sg/home/ehchua/programming/cpp/gcc_make.html - various interesting notes

Cuda/OpenCL

1. Поддерживаемые устройства Nvidia для работы с Cuda

2. CUDA - habrahabr

3. Поддерживаемые устройства Ati для работы с OpenCL

4. OpenCL: Ati Radeon VS Nvidia

Software development and programming

1. Rreverse-Engineering.info ресурс по реверс инженерингу под винду

2. Аггрегатор компьютерных курсов Стенфорд, Принстон

3. Сервис создания png картинок для tex формул

4. Графики по популярности языков программирования

5. Журнал drdobbs

6. Float point number comparision

7. Reverse Engineering for ARM Platforms Course Videos - https://www.pentesteracademy.com/course?id=49

8. ARM Assembly Course Videos - https://www.pentesteracademy.com/course?id=46

Tools

1. Инструмент создания картинок по *tex подобным формулам

2. Ttohtml. code highlighting

3. 123dapp from autodesk

4. BBFlashBack free, screen recording

5. https://web.stanford.edu/class/archive/cs/cs107/cs107.1166/guide_callgrind.html

6. https://web.stanford.edu/class/archive/cs/cs107/cs107.1174/guide_gdb.html

7. https://web.stanford.edu/class/archive/cs/cs107/cs107.1174/guide_valgrind.html

8. https://github.com/Microsoft/PTVS/releases/v2.1.1

Educational courses and journals

1. http://academictorrents.com - Torrent dedicated for courses/papers

2. Курсы по английскому для детей и не только

Physics

1. Мифи. Макеев, тер. мех

2. Страничка Маркеева А.П.

3. Механика сплошных сред (Жмур Владимир Владимирович)

4. Оптика (Козел Станислав Миронович)

5. Вычислительная математика

6. Лекции по физике для полуновичков от Леонард Сасскинда

7. https://graphics.stanford.edu/courses/cs178-10/lectures/optics1-06apr10-150dpi-med.pdf

8. Лекции Ramamurti Shankar по общей физике

9. http://nptel.ac.in/courses/101108056/1 - что-то про радиолокацию

10. Библиотека Мехмата. Книги разыскиваемые в электронном виде.

11. http://www.sciencealert.com/all-of-richard-feynmans-physics-lectures-are-now-available-free-online - Collection of R.Feynman lectures

Computer graphics courses

1. Некоторые Материалы Мичиганского Технического Университета по компьютерной графике

2. Carnegie Mellon Graphics courses

3. Stanford graphics courses

4. Caltech CS146

5. Computer Graphics Course. University of Southern California

6. https://www.cs.utexas.edu/~fussell/

7. https://www.cs.utexas.edu/~fussell/courses/cs384g/lectures/

8. http://web.stanford.edu/class/cs148/

9. http://web.stanford.edu/class/cs248/

10. http://graphics.stanford.edu/courses/cs348b/

11. http://graphics.stanford.edu/courses/

12. https://graphics.stanford.edu/courses/

13. http://www.eng.utah.edu/~cs5600/slides/

14. http://www.minho-kim.com/courses/13fa71033/data/redbook-01.pdf

15. http://web.engr.oregonstate.edu/~mjb/cs557/Handouts/

SIGGRAPH Resources

1. http://kesen.realtimerendering.com/

2. http://s2016.siggraph.org/courses

Machine learning

1. https://kth.instructure.com/courses/5546/pages/lectures - materials from KTH university, contain some technical details which can be usefull for own neural network framework creation.

2. https://www.cs.utexas.edu/~mooney/

3. http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/

4. http://www.cs.utah.edu/~piyush/teaching/cs5350.html

5. CS231N Recorded Playlist from YouTube

6. http://statistics.berkeley.edu/courses/spring-2016/spring-2016-stat-212b-001-lec

7. https://github.com/joanbruna/stat212b

8. http://rll.berkeley.edu/deeprlcourse/

9. http://ee104.stanford.edu/lectures.html

10. CS231N, 2017, Spring  - https://www.youtube.com/playlist?list=PLC1qU-LWwrF64f4QKQT-Vg5Wr4qEE1Zxk

11. http://yann.lecun.com/exdb/mnist/?fbclid=IwAR22Nr-Y7Rb5LSO_HDJn9fhtyn5ICl57o7Tx01PJ1iwtIL8jtm2QG_4NCvw

12. http://web.stanford.edu/class/cs20si/

13. http://graphics.stanford.edu/courses/cs348v-18-winter/ - hardware aspects for deep-learning

14. http://graphics.stanford.edu/courses/cs348v-18-winter/lectures/08_dnneval.pdf - about convolutions in CNNs

15. https://web.stanford.edu/class/cs245/spr2019/ -  Principles of Data-Intensive Systems include slydes of Graph Optimization in Tensor Flow

Some new Machine Learning courses from Stanford

• 1. CS224W: Machine Learning with Graphs                      http://web.stanford.edu/class/cs224w/

  2. CS224U: Natural Language Understanding                    https://web.stanford.edu/class/cs224u/index.html

  3. CS236: Deep Generative Models                                    https://deepgenerativemodels.github.io/syllabus.html

  4. CS 330: Deep Multi-Task and Meta-Learning                https://cs330.stanford.edu/

1. CS246: Mining Massive Data Sets                                  http://web.stanford.edu/class/cs246/

   1. CS 228 - Probabilistic Graphical Models                        https://cs228.stanford.edu/

   2. CS234: Reinforcement Learning                                     http://web.stanford.edu/class/cs234/index.html

   3. CS331B Representation Learning in Computer Vision   http://web.stanford.edu/class/cs331b/schedule.html

Algorithms

1. https://www.coursera.org/course/algs4partII

2. https://www.coursera.org/course/algs4partI

3. https://class.coursera.org/algo-003/lecture

4. https://class.coursera.org/algo2-2012-001/lecture

5. http://www.mkurnosov.net/

6. http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-851-advanced-data-structures-spring-2012/calendar-and-notes/

7. http://courses.csail.mit.edu/6.006/spring11/notes.shtml

Computer Vision

1. http://vision.princeton.edu/courses/COS429/2014fa/

2. http://cs.brown.edu/courses/cs143/

3. Andrew Zisserman's lectures

4. Harris corner detector. Intersting Lecture note.

5. http://www.cs.toronto.edu/pub/jepson/teaching/vision/2503/

6. http://www.cs.cmu.edu/afs/cs/academic/class/15385-s12/www/lec_slides/ - Carnegie Mellon course

Computer vision papers

1. http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Revaud_EpicFlow_Edge-Preserving_Interpolation_2015_CVPR_paper.pdf

2. http://lear.inrialpes.fr/src/epicflow/

Machine Learning

1. http://cs.stanford.edu/~quocle/tutorial1.pdf

2. http://cs.stanford.edu/~quocle/tutorial2.pdf

3. http://cs.stanford.edu/~quocle/

4. http://web.stanford.edu/class/cs20si/syllabus.html

5. http://cs231n.stanford.edu/syllabus.html

6. MIT 6.S094: Deep Learning for Self-Driving Cars

7. http://snap.stanford.edu/class/cs246-videos-2018/

8. http://web.stanford.edu/class/cs246/index.html#schedule

9. https://www.skoltech.ru/app/data/uploads/2018/10/Thesis-Final.pdf

PyTorch Relative

1. https://github.com/pytorch/glow/blob/master/docs/Optimizations.md

2. https://ieee-region6.org/scv-cis/wp-content/uploads/sites/6/2019/03/IEEE_Glow.pdf

3. https://www.infoq.com/presentations/pytorch-torchscript-botorch/

4. https://pytorch.org/tutorials/

Math Optimization Courses

1. http://www.princeton.edu/~yc5/ele522_optimization/lectures.html -- ELE522: Large-Scale Optimization for Data Science

Математика / Math

1. Д.О. Орлов. Проективные плоскости и алгебраическая геометрия

2. Вычислительная математика

3. Обзор алгоритмов численной оптимизации в одной лекции (OxFord, Andrew Zisserman's, C25 Optimization lectures Hilary 2013)

4. https://www-old.math.gatech.edu/academic/courses/core/math2601/Web-notes/ -

5.  численные методы решения некоторых задач в лин.алгебре

6. Ширяев, Альберт Николаевич - автор ряда трудов по теории вероятности и математической статистики

7. Хелемский. Лекции по функциональному анализу

8. Мировой эксперт по оптимизации - Б. Поляк

9. http://www.convexoptimization.com/wikimization/index.php/Optimization_Videos - подборка материалов по оптимизации из MIT

10. https://www.sms.cam.ac.uk/media/1117768 - Emanuel Candes Videos abot compress sensing

11. http://ilib.mccme.ru/ - золотой фонд популярной физико-математической литературы

12. http://ilib.mccme.ru/plm/ - популярные лекции по математике

C++

1. http://www.stroustrup.com/C++11FAQ.html

2. https://isocpp.org/wiki/faq

3. https://channel9.msdn.com/Events/CPP/CppCon-2016/

4. https://cppcon.org/history/

5. http://www.open-std.org/jtc1/sc22/wg21/docs/standards C++ standarts

6. https://www.agner.org/optimize/ - Agner Fog's optimization manuals

Qt

1. https://www.youtube.com/watch?v=HYBLfsO2Vng

2. https://www.qt.io/training-materials/

3. https://woboq.com/blog/new-signals-slots-syntax-in-qt5.html

4. https://stanford.edu/class/archive/cs/cs106x/cs106x.1172/handouts/DebuggingWithQtCreator.pdf

5. http://www.comp.nus.edu.sg/~cs3249/lecture/

About game development

1. https://software.intel.com/gamedev/journey/ideate?utm_source=Facebook&utm_medium=banner&utm_content=General_App_Enthusiasts_EMEA&utm_campaign=DRD_16_100

About networks

1. https://itunes.apple.com/us/course/computer-networks/id502504690

Stanford relative links

1. https://www.quora.com/What-is-it-like-to-study-robotics-at-Stanford

2. http://anie.me/Lagrangian-And-Dual-Problem/ - blog of TA from NLP

Robotics

1. https://www.coursera.org/specializations/robotics

2. http://cs.stanford.edu/groups/manips/teaching/cs223a/

Numerical linear algebra

1. BLAS and LAPACK functions

2. https://www.gnu.org/software/gsl/manual/html_node/GSL-CBLAS-Library.html 

3. http://www.icl.utk.edu/~mgates3/docs/lapack.html

4. http://cvxopt.org/userguide/lapack.html#general-linear-equations

5. http://www.netlib.org/lapack/ - LAPACK library original

6. http://www.netlib.org/blas/ - BLAS library original

Compilers:

• 1. https://youtu.be/8x057jVBCVM - Compiler Construcction from California State University

Tools

Collection of tutorials

- TutorialsTeacher - Learn Web Technologies

Tools Tutorials

1. Conda Tutorial-1 https://www.youtube.com/watch?v=clKgaEZJYww

2. Conda Tutorial-2 https://www.youtube.com/watch?v=ZXrUfNwPOe4