- 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&rep=rep1&t
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
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Class Rectangle | |
val rectWrites = ( | |
(__ \ 'width).write[Long] and | |
(__ \ 'height).write[Long] and | |
(__ \ 'x).write[Long] and | |
(__ \ 'y).write[Long] | |
)( (r: Rectangle) => (r.width, r.height, r.x, r.y) ) |
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mon implementation à moi qui tue! |
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-- Type-aligned sequence catenable queue supporting O(1) append, snoc, uncons | |
-- Don't need it to be a dequeue (unsnoc not needed) | |
data TCQueue c a b -- c is the category, a is starting type, b is ending type | |
type Channel f a b = TCQueue (Transition f) a b | |
type Process f b = Channel f () b | |
data Transition f a b where | |
Bind :: (a -> Process f b) -> Transition f a b | |
OnHalt :: (Cause -> Process f b) -> Transition f a b |