Machine Learning: Difference between revisions
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* [http://caffe.berkeleyvision.org/ Caffe deep learning framework] | * [http://caffe.berkeleyvision.org/ Caffe deep learning framework] | ||
* SystemML- a Universal Translator for Big Data and Machine Learning | |||
** http://www.ibm.com/blogs/think/2015/11/24/introducing-a-universal-translator-for-big-data-and-machine-learning/ | |||
** https://github.com/SparkTC/systemml/ | |||
** http://researcher.watson.ibm.com/researcher/view_group.php?id=3174 | |||
** https://developer.ibm.com/open/systemml/ | |||
== sample data == | == sample data == |
Revision as of 14:49, 2 December 2015
getting started
google://getting started with machine learning
https://www.kaggle.com/wiki/GettingStartedWithPythonForDataScience - in progress
https://www.quora.com/I-want-to-learn-machine-learning-Where-should-I-start
http://thunderboltlabs.com/blog/2013/11/09/getting-started-with-machine-learning/
http://machinelearningmastery.com/machine-learning-for-programmers/
https://www.kaggle.com/dfernig/reddit-comments-may-2015/the-biannual-reddit-sarcasm-hunt/code
course: at coursera https://www.coursera.org/learn/machine-learning/home/week/1
understanding machine learning theory algorithms
algorithms
random forest
https://medium.com/rants-on-machine-learning/the-unreasonable-effectiveness-of-random-forests-f33c3ce28883
tools
python + libs
- SystemML- a Universal Translator for Big Data and Machine Learning