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## gruln.py
import numpy as np
from keras.layers import GRU, initializations, K
from collections import OrderedDict

class GRULN(GRU):
    '''Gated Recurrent Unit with Layer Normalization
    Current impelemtation only works with consume_less = 'gpu' which is already
    set.

    # Arguments

## 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
	import numpy as np
	from keras.layers import GRU, initializations, K
	from collections import OrderedDict

	class GRULN(GRU):
	'''Gated Recurrent Unit with Layer Normalization
	Current impelemtation only works with consume_less = 'gpu' which is already
	set.

	# Arguments