Difference between revisions of "ML curriculum"
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* [https://pandas.pydata.org/pandas-docs/stable/10min.html 10 minutes to pandas] | * [https://pandas.pydata.org/pandas-docs/stable/10min.html 10 minutes to pandas] | ||
* [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] ##machinelearning's intro articles, esp math. | ||
== 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. | ||
* Later think about [https://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html/2 this], but I'm not sure I like those links. TODO | * Later think about [https://www.kdnuggets.com/2015/11/seven-steps-machine-learning-python.html/2 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" | ||
<hr> | <hr> | ||
Revision as of 14:13, 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
- [1] ##machinelearning's intro articles, esp math.
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
- [2] Coursera articles, again from #machinelearning
- [3] Google ML crash course
- [4] Main ##ml "Main"
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
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