- 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
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""" 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 |
https://web.stanford.edu/class/cs25/
https://www.youtube.com/watch?v=mcep6W8oB1I https://www.youtube.com/watch?v=fz8wf9hN20c https://www.youtube.com/watch?v=wwQ1LQA3RCU https://www.youtube.com/watch?v=1GbDTTK3aR4 https://www.youtube.com/watch?v=ylEk1TE1uBo https://www.youtube.com/watch?v=mE7IDf2SmJg https://www.youtube.com/watch?v=ckNMsUuLryM