iuga

<script src="https://api.labelbox.com/static/labeling-api.js"></script>
<div id="form"></div>
<script>
function label(label){
  Labelbox.setLabelForAsset(label).then(() => {
    Labelbox.fetchNextAssetToLabel();
  });
}

Labelbox.currentAsset().subscribe((asset) => {

iuga

# Defining the sigmoid function for activations
def sigmoid(x):
    return 1/(1+np.exp(-x))

# Derivative of the sigmoid function
def sigmoid_prime(x):
    return sigmoid(x) * (1 - sigmoid(x))

x = np.array([0.1, 0.3])
y = 0.2

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# Note – this is not a bash script (some of the steps require reboot)
# I named it .sh just so Github does correct syntax highlighting.
#
# This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5
#
# The CUDA part is mostly based on this excellent blog post:
# http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/

# Install various packages
sudo apt-get update

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def get_media_count_by_customer(self, customer_id):
    """
    Get the media OK count by customer for all the active customers
    Query:
    SELECT customer_id, status, COUNT(id) FROM pr_medias GROUP BY customer_id, status HAVING status = 'OK'
    :return: Customer List
    """
    with self.olapic_dao.create_session() as session:
        return session.query(
            Media.customer_id, Media.status, func.count(Media.id)

iuga

1. 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&rep