Data is big
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Data is big
&amp;amp;quot;The future is here. It's just not evenly distributed yet.&amp;amp;quot; - William Gibson     :::: Follow this topic for fresh resources and ideas related to Data Science, Machine Learning, Algorithms and #bigdata :::: <a href="http://www.dataisbig.co" rel="nofollow">http://www.dataisbig.co</a>/
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Introduction to Neural Machine Translation with GPUs (part 1)

Introduction to Neural Machine Translation with GPUs (part 1) | Data is big | Scoop.it
Neural machine translation is a recently proposed framework for machine translation based purely on neural networks.  
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Roundup Of Analytics, Big Data & Business Intelligence Forecasts And Market Estimates, 2015

Roundup Of Analytics, Big Data & Business Intelligence Forecasts And Market Estimates, 2015 | Data is big | Scoop.it

Customer analytics (48%), operational analytics (21%), and fraud & compliance (21%) are the top three use cases for Big Data. Datameer’s analysis of the market also found that the global Hadoop market will grow from $1.5B in 2012 to $50.2B in 2020, and financial services, technology and telecommunications are the leading industries using big data solutions today

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Stigler Diet – How I Became a Data Scientist Despite Having Been a Math Major

Posts and writings by Tim Hopper
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Top 10 data mining algorithms in plain English

Top 10 data mining algorithms in plain English | Data is big | Scoop.it
Today, I’m going to explain in plain English the top 10 most influential data mining algorithms as voted on by 3 separate panels in this 2007 survey paper.
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Predicting a Billboard Music Hit with Shazam Data - Cait O'Riordan

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Best R packages for data import, data wrangling & data visualization

Best R packages for data import, data wrangling & data visualization | Data is big | Scoop.it
Useful R packages in a handy searchable table
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Profiling Top Kagglers: KazAnovaCurrently #2 in the World

Profiling Top Kagglers: KazAnovaCurrently #2 in the World | Data is big | Scoop.it

There are Kagglers, there are Master Kagglers, and then there are top 10 Kagglers. Who are these people who consistently win Kaggle competitions? 

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Comprehensive guide for Data Exploration in R

Comprehensive guide for Data Exploration in R | Data is big | Scoop.it
This guide explains how to read data set in R, explore data in R, impute missing values in your dataset, visualize the dataset & merge in R
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pandas: powerful Python data analysis toolkit [Free 1375-page book]

pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. 

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Free 1375-page book! >> #Python Pandas data analysis toolkit for#DataScience: bit.ly/1HGtBCr  via @KirkDBorne 
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Interactive R Tutorial: Machine Learning for the Titanic Competition

Interactive R Tutorial: Machine Learning for the Titanic Competition | Data is big | Scoop.it
Always wanted to compete in a Kaggle competition, but not sure you have the right skill set? At DataCamp we created a free interactive tutorial to help you out!  
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Together with the team at Kaggle, we have developed this tutorial on how to apply Machine Learning techniques. Step by step, through fun coding challenges, the tutorial will teach you how to predict survival rate for Kaggle's Titanic competition using R and Machine Learning. The skills you'll learn in the tutorial can be applied across your Kaggle competitions

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Random Thoughts : Learning Deep Learning

A wiki of my random thoughts.
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There are lots of awesome reading lists or posts that summarized materials related to Deep Learning. So why would I commit another one? Well, the primary objective is to develop a complete reading list that allows readers to build a solid academic and practical background of Deep Learning. And this list is developed while I'm preparing my Deep Learning workshop. My research is related to Deep Neural Networks (DNNs) in general. Hence, this posts tends to summary contributions in DNNs instead of generative models.

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How Stephen Wolfram’s image-recognition tool performs against 5 alternatives

How Stephen Wolfram’s image-recognition tool performs against 5 alternatives | Data is big | Scoop.it
To get a feel for the power of the new Wolfram technology, I put it up against other image-recognition systems.
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Bringing Life to Design: Data Science in 3D - Autodesk's Mike Haley

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Time-lapse Mining from Internet Photos [SIGGRAPH 2015] - YouTube

Project website: http://grail.cs.washington.edu/projects/timelapse/ We introduce an approach for synthesizing time-lapse videos of popular landmarks from lar...
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What Police Can Learn from Deep Learning

What Police Can Learn from Deep Learning | Data is big | Scoop.it

Police departments are increasingly turning to predictive analytics to help them fight crime, and the early returns are positive, with double-digit drops in crime rates reported in many cities. So what’s next? According to big data analytics experts, police departments could spend their time and money more effectively by giving deep learning algorithms a role in the dispatch room.

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Methods to find the most important variables for building models in R

Methods to find the most important variables for building models in R | Data is big | Scoop.it
"Selecting the most important predictor variables that explains the major part of variance of the response variable can be key to identify and build high performing models. These techniques are powerful tools that can help reveal the large sediments of gold in your data.

Random Forest
Rando
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