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Scikit-learn, machine learning and cybercrime attribution

Scikit-learn, machine learning and cybercrime attribution | Data is big | Scoop.it
Robert Layton http://2013.pycon-au.org/schedule/30019/view_talk The scikit-learn library is a rapidly growing open source toolkit for machine learning in python.
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luiy's curator insight, July 16, 2013 5:51 AM

 

The scikit-learn library is a rapidly growing open source toolkit for machine learning in python. It allows for practitioners and researchers to apply machine learning in a variety of applications and is used by companies worldwide. Developed by programmers from around the world, the project has a large (and increasing) number of machine learning algorithms, a very useful set of utility functions and has also spawned a set of detail

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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The complete catalog of argument variations of select() in dplyr

The complete catalog of argument variations of select() in dplyr | Data is big | Scoop.it

# Data preparation ------------------------------------------------------------------
library(dplyr)

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The Unreasonable Effectiveness of Recurrent Neural Networks

The Unreasonable Effectiveness of Recurrent Neural Networks | Data is big | Scoop.it
Musings of a Computer Scientist.
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We've learned about RNNs, how they work, why they have become a big deal, we've trained an RNN character-level language model on several fun datasets, and we've seen where RNNs are going. You can confidently expect a large amount of innovation in the space of RNNs, and I believe they will become a pervasive and critical component to intelligent systems.

 

  
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List of amazing talks from New York R Conference 2015

List of amazing talks from New York R Conference 2015 | Data is big | Scoop.it
From NewYork R conference 2015, here is a list of amazing videos which describes about various application of R i.e. statistics, machine learning
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Text Mining of The Complete Works of Jane Austen

Text Mining of The Complete Works of Jane Austen | Data is big | Scoop.it
Text mining refers to extraction of meaningful information from qualitative and unstructured text data. In this document we will perform text mining on The Complete Works of Jane Austen. We can tak...
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Microservices, containers, and machine learning

http://www.oscon.com/open-source-2015/public/schedule/detail/41579 In this presentation, an open source developer community considers itself algorithmically. T…
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15 Easy Solutions To Your Data Frame Problems In R

15 Easy Solutions To Your Data Frame Problems In R | Data is big | Scoop.it
Discover how to create a data frame in R, change column and row names, access values, attach data frames, apply functions and much more.
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Social media fingerprints of unemployment in Spain - Netsci 2015

Presentation about how social media information about users allows to model economical features such as unemployment rate in Spain.
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R tutorial on the Apply family of functions

R tutorial on the Apply family of functions | Data is big | Scoop.it
Introduction In our previous tutorial Loops in R: Usage and Alternatives , we discussed one of the most important constructs in programming: the loop.  Eventually we deprecated the usage of loops in R in favor of vectorized functions. In this post we highlight some of the most used vectorized functions: the apply functions.  
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Demis Hassabis (CEO, DeepMind Technologies) - The Theory of Everything and AI learning to play Atari games

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Machine Learning Class 2015 - University of Oxford

Machine Learning Class 2015 - University of Oxford | Data is big | Scoop.it
Website for the Department of Computer Science at the heart of computing and related interdisciplinary activity at Oxford.
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Strategies To Speed Up R Code

Strategies To Speed Up R Code | Data is big | Scoop.it
The for-loop in R, can be very slow in its raw un-optimised form, especially when dealing with larger data sets. There are a number of ways you can make your logics run fast, but you will be really surprised how fast you can actually go.
This chapter shows a number of approaches including simple
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Sparkling Water Applications Meetup 07.21.15

Michal Malohlava's Sparkling Water Applications Meetup on 07.21.15, focusing on the Ask Craig use case. http://h2o.ai/blog/2015/06/ask-craig-sparkling-water/
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GitHub Special: Data Scientists to Follow & Best Tutorials on GitHub

GitHub Special: Data Scientists to Follow & Best Tutorials on GitHub | Data is big | Scoop.it
GitHub has some of the most awesome collections of data science resources. This article provides this list and people to follow on GitHub
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First computers recognized our faces, now they know what we’re doing

We haven't designed fully sentient artificial intelligence just yet, but we're steadily teaching computers how to see, read, and understand our world. 

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 But the proliferation of this machine learning has a creepy side too — if your computer can work out exactly what's happening in your pictures, what happens when it works out exactly what you are?

 
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NIPS 2013 Tutorial - Deep Learning for Computer Vision (Rob Fergus)

NIPS 2013 Tutorial - Deep Learning for Computer Vision (Rob Fergus) | Data is big | Scoop.it

 Slides: http://media.nips.cc/Conferences/2013/Video/Tutorial1A.pdf

This tutorial will look at how deep learning methods can be applied to
problems in computer vision, most notably object recognition. It will start by
motivating the need to learn features, rather than hand-craft them. It will
then introduce several basic architectures, explaining how they learn
features, and showing how they can be "stacked" into hierarchies that can
extract multiple layers of representation.

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Throughout, links will be drawn between these methods and existing approaches to recognition, particularly those involving hierarchical representations. The final part of the lecture will examine the current performances obtained by feature learning approaches on a range of standard vision benchmarks, highlighting their strengths and weaknesses. The tutorial will conclude with a discussion of vision problems that have yet to be successfully addressed by deep learning.

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