Ju Yang yangju2011

## auto-sklearn.txt
# Create a conda environment
conda create -n bayes
source activate bayes

# Get a new version of gcc from anaconda
conda install gcc

# Get Auto-Sklearn and the terribly stubborn pyrfr libraries
curl https://raw.githubusercontent.com/automl/auto-sklearn/master/requirements.txt | xargs -n 1 -L 1 pip install

## gist:8172796

      
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                A collection of links for streaming algorithms and data structures
              
          
    General Background and Overview


Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
Models and Issues in Data Stream Systems
Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
[Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&amp;rep=rep1&amp;t
	# Create a conda environment
	conda create -n bayes
	source activate bayes

	# Get a new version of gcc from anaconda
	conda install gcc

	# Get Auto-Sklearn and the terribly stubborn pyrfr libraries
	curl https://raw.githubusercontent.com/automl/auto-sklearn/master/requirements.txt \| xargs -n 1 -L 1 pip install