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import logging
import matplotlib.pyplot as plt
import numpy as np
import os
import scipy.stats as stats
import sys


def read_data(filename):
    """Reads a data file assumed to have at least 2 columns: 1) lat, 2) lng."""

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Putting wings on the Elephant

[operating-hadoop]

HBase is used widely at Facebook and one of the biggest usecase is Facebook Messages. With a billion users there are a lot of reliability and performance challenges on both HBase and HDFS. HDFS was originally designed for a batch processing system like MapReduce/Hive. A realtime usecase like Facebook Messages where the p99 latency can`t be more than a couple hundreds of milliseconds poses a lot of challenges for HDFS. In this talk we will share the work the HDFS team at Facebook has done to support a realtime usecase like Facebook Messages : (1) Using system calls to tune performance; (2) Inline checksums to reduce iops by 40%; (3) Reducing the p99 for read and write latencies by about 10x; (4) Tools used to determine root cause of outliers. We will discuss the details of each technique, the challenges we faced, lessons learned and results showing the impact of each improvement.

speaker: Pritam Damania

Real-Time Market Basket Analysis for Retail with

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Machine learning

http://scikit-learn.org/stable/related_projects.html

From zero to storm cluster for scikit-learn classification

Deep learning

http://deeplearning.net/software_links/

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• A Statistical View of Deep Learning
• What My Deep Model Doesn't Know...
• Bayesian Methods for Machine Learning
• Deep Learning Summer School 2015
• Bengio's Deep learning course notes
• deeplearning.net tutorial
• UFLDL Tutorial
• Reading lists for new MILA students
• [What's the best way to go about transitioning to a ML career? Is it even realistic for someone with my background?](https://www.reddit.com/r/MachineLearning/comments/3sknex/whats_the_bes

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# Requires pydub (with ffmpeg) and Pillow
#
# Usage: python waveform.py <audio_file>

import sys

from pydub import AudioSegment
from PIL import Image, ImageDraw

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Movies Recommendation:

• MovieLens - Movie Recommendation Data Sets http://www.grouplens.org/node/73
• Yahoo! - Movie, Music, and Images Ratings Data Sets http://webscope.sandbox.yahoo.com/catalog.php?datatype=r
• Jester - Movie Ratings Data Sets (Collaborative Filtering Dataset) http://www.ieor.berkeley.edu/~goldberg/jester-data/
• Cornell University - Movie-review data for use in sentiment-analysis experiments http://www.cs.cornell.edu/people/pabo/movie-review-data/

Music Recommendation:

• Last.fm - Music Recommendation Data Sets http://www.dtic.upf.edu/~ocelma/MusicRecommendationDataset/index.html