Bleyddyn Bleyddyn

These links no longer work. Springer have pulled the free plug.

duplicates = multiple editions

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


	class MultiInputProcessor(Processor):
	"""Converts observations from an environment with multiple observations for use in a neural network
	policy.
	In some cases, you have environments that return multiple different observations per timestep
	(in a robotics context, for example, a camera may be used to view the scene and a joint encoder may
	be used to report the angles for each joint). Usually, this can be handled by a policy that has
	multiple inputs, one for each modality. However, observations are returned by the environment
	in the form of a tuple `[(modality1_t, modality2_t, ..., modalityn_t) for t in T]` but the neural network
	expects them in per-modality batches like so: `[[modality1_1, ..., modality1_T], ..., [[modalityn_1, ..., modalityn_T]]`.

	"""
	Clean and simple Keras implementation of network architectures described in:
	- (ResNet-50) [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf).
	- (ResNeXt-50 32x4d) [Aggregated Residual Transformations for Deep Neural Networks](https://arxiv.org/pdf/1611.05431.pdf).

	Python 3.
	"""

	from keras import layers
	from keras import models

	""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
	import numpy as np
	import cPickle as pickle
	import gym

	# hyperparameters
	H = 200 # number of hidden layer neurons
	batch_size = 10 # every how many episodes to do a param update?
	learning_rate = 1e-4
	gamma = 0.99 # discount factor for reward

	#!/usr/bin/env python
	from Foundation import NSUserNotification
	from Foundation import NSUserNotificationCenter
	from Foundation import NSUserNotificationDefaultSoundName
	from optparse import OptionParser



	def main():
	parser = OptionParser(usage='%prog -t TITLE -m MESSAGE')