Difference between revisions of "ML curriculum"
From Fiamma
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* [http://www.scipy-lectures.org/ Scipy lecture notes] -- looks excellent | * [http://www.scipy-lectures.org/ Scipy lecture notes] -- looks excellent | ||
* [https://github.com/p-i-/machinelearning-IRC-freenode/blob/master/Resources/ArticlesIntroductory.md ArticlesIntroductiory] ##machinelearning's intro articles, esp math. | * [https://github.com/p-i-/machinelearning-IRC-freenode/blob/master/Resources/ArticlesIntroductory.md ArticlesIntroductiory] ##machinelearning's intro articles, esp math. | ||
| + | * http://www.deeplearningbook.org/ nice intro book | ||
| + | * [https://github.com/ZuzooVn/machine-learning-for-software-engineers/blob/master/README.md ML for Software Engineers] also looks wonderful | ||
== More interesting stuff == | == More interesting stuff == | ||
* [http://www.holehouse.org/mlclass/ Andrew Ng's Stanford ML course, unofficial notes] as Ersatz for the Coursera video-course. | * [http://www.holehouse.org/mlclass/ Andrew Ng's Stanford ML course, unofficial notes] as Ersatz for the Coursera video-course. | ||
| Line 13: | Line 15: | ||
* https://ai.google/education google AI | * https://ai.google/education google AI | ||
* [https://github.com/josephmisiti/awesome-machine-learning Awesome machine learning] | * [https://github.com/josephmisiti/awesome-machine-learning Awesome machine learning] | ||
| + | * https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md | ||
<hr> | <hr> | ||
| Line 48: | Line 51: | ||
++++++++курс на курсере конечно | ++++++++курс на курсере конечно | ||
</pre> | </pre> | ||
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Revision as of 14:23, 16 May 2018
Using this as base:
Basics
- Short intro to Python in 60 pages, 50% done.
- 10 minutes to pandas
- Scipy lecture notes -- looks excellent
- ArticlesIntroductiory ##machinelearning's intro articles, esp math.
- http://www.deeplearningbook.org/ nice intro book
- ML for Software Engineers also looks wonderful
More interesting stuff
- Andrew Ng's Stanford ML course, unofficial notes as Ersatz for the Coursera video-course.
- Later think about this, but I'm not sure I like those links. TODO
- https://github.com/p-i-/machinelearning-IRC-freenode/blob/master/Resources/GettingStarted.md Coursera articles, again from #machinelearning
- https://developers.google.com/machine-learning/crash-course/ Google ML crash course
- https://github.com/p-i-/machinelearning-IRC-freenode/blob/master/Resources/Main.md Main ##ml "Main"
- https://ai.google/education google AI
- Awesome machine learning
- https://github.com/floodsung/Deep-Learning-Papers-Reading-Roadmap/blob/master/README.md
Мария, [05.03.18 21:55] - TensorFlow https://www.tensorflow.org/ -- Шарить что такое граф -- Шарить как работать с сессией (как создавать, как запускать операции на выполнение) -- Понимать вообще глобальную архитектуру приложения на TensorFlow -- Хоть чуть-чуть иметь представление как делать backprop через граф - Как дебажить обучение -- Понять из графика лосса что происходит с обучением -- Overfitting, underfitting, vanishing/exploding gradient - Алгоритмы оптимизации -- SGD, SGD with momentum, RMSProp, Adam - Современные архитектуры -- VGG, Segnet, ResNet, Inception, Inception-ResNet, NasNet -- Понимать зачем нужна каждая из них и для чего ее создавали - OpenCV -- Гистограмное выравнивание -- Cascade Classifier - dlib -- Просто знать что там можно искать лица и ключевые точки лица - Python -- Библиотеки numpy, matplotlib -- Уметь грузить/сохранять файлы Мария, [05.03.18 21:55] ++++++++курс на курсере конечно
