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If this doesn't terrify you... Google's computers OUTWIT their humans

If this doesn't terrify you... Google's computers OUTWIT their humans | Data is big | Scoop.it
Given Google's ambition to "organize the world's information", the fewer people it needs to employ, the better. By developing these "deep learning" systems Google needs to employ fewer human experts, Quoc, said. "Machine learning can be difficult because it turns out that even though in theory you could use logistic regression and so on, but in practice what happens is we spend a lot of time on data processing inventing features and so on. For every single problem we have to hire domain experts," he added. "We want to move beyond that ... there are certainly problems we can't engineer features of and we want machines to do that."
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The future is here, it's just not evenly distributed.
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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 affinity propagation clustering with apcluster package

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This is the complete recording of a webinar on the R package "apcluster" by the maintainer and co-developer of the package, Ulrich Bodenhofer (Institute of Bioinformatics, Johannes Kepler University, Linz, Austria). The "apcluster" package implements affinity propagation clustering along with tools for analyzing and visualizing results.

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How does a relational database work

How does a relational database work | Data is big | Scoop.it
An in-depth article that explains how a relational database handles an SQL query and the basic components inside a database.
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Hadleyverse: A Pointless R Package

I recently wrote a silly R package as a practical joke.  It's called "Hadleyverse." It just attaches Hadley Wickham's packages.

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When you install an R Package, R checks the DESCRIPTION file for dependencies. If you have unmet dependencies, R tries to install them from CRAN. Then, whenever you load the package, R makes those dependencies available. This package just “depends on” everything Hadley Wickham has published to CRAN, despite the fact that it doesn’t do anything. Here are the packages it loads:

plyrggplot2dplyrtidyrreadrhavenlubridatestringrreadxldevtoolsxml2testthatassertthat
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Deploy your Shiny App on AWS with a Function

Deploy your Shiny App on AWS with a Function | Data is big | Scoop.it
ramazon is an R package that lets you deploy a shiny app on Amazon AWS just running a function.
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Automatic Forecasting at Scale: [#JSM2015 slides] #BigData #MachineLearning #DataScience by @seanjtaylor

Automatic Forecasting at Scale:  [#JSM2015 slides] #BigData #MachineLearning #DataScience by @seanjtaylor | Data is big | Scoop.it
Data is big. Powered by RebelMouse
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Composing Music With Recurrent Neural Networks

Composing Music With Recurrent Neural Networks | Data is big | Scoop.it
It's hard not to be blown away by the surprising power of neural networks these days. With enough training, so called "deep neural networks", with many nodes and hidden layers, can do impressively ...
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#DeepLearning for Perception - Virginia Tech Course [Fall 2015]

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This is an exciting time to be studying (Deep) Machine Learning, or Representation Learning, or for lack of a better term, simply Deep Learning!

 

Deep Learning is rapidly emerging as one of the most successful and widely applicable set of techniques across a range of applications (vision, language, speech, computational biology, robotics, etc), leading to some pretty significant commercial success.

This course will expose students to cutting-edge research — starting from a refresher in basics of neural networks, to recent developments. The emphasis will be on student-led paper presentations and discussions. Each “module” will begin with instructor lectures to present context and background material.

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Building Web Data Products with R & Shiny | Codementor

Building Web Data Products with R & Shiny | Codementor | Data is big | Scoop.it
Codementor provides instant 1:1 help for software development. Build projects faster with on-demand live help from expert developers.
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Distributed PageRank Using Apache Spark and Neo4j

Distributed PageRank Using Apache Spark and Neo4j | Data is big | Scoop.it
How to build a scalable distributed PageRank scheduler using open source technologies like Neo4j and Apache Spark.
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Data Scientist Shares his Growth Hacking Secrets

Data Scientist Shares his Growth Hacking Secrets | Data is big | Scoop.it
In this article, we discuss various strategies used to generate exponential traffic growth, while preserving traffic quality, and user loyalty. Our growth hack…
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Visualising thefts using heatmaps in ggplot2 | DataScience+

Visualising thefts using heatmaps in ggplot2 | DataScience+ | Data is big | Scoop.it
Project-based tutorials with step-by-step instructions and examples. Practice your skills from the basic to advanced R programming, across different topics including statistics, data manipulation and visualization!
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A 5 STEP CHECKLIST FOR MULTIPLE LINEAR REGRESSION

A 5 STEP CHECKLIST FOR MULTIPLE LINEAR REGRESSION | Data is big | Scoop.it
Get a full-scale walk thru of the 5 step process for multiple regression analysis. Don't forget to grab the free regression checklist download.
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Visualizing Parliamentary Discourse with Word2Vec and Gephi. | Digging into Linked Parliamentary Data

Visualizing Parliamentary Discourse with Word2Vec and Gephi. | Digging into Linked Parliamentary Data | Data is big | Scoop.it
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