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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A Gentle Introduction to Finance using R: Efficient Frontier and CAPM – Part 1

A Gentle Introduction to Finance using R: Efficient Frontier and CAPM – Part 1 | Data is big | Scoop.it
The following entry explains a basic principle of finance, the so-called efficient frontier and thus serves as a gentle introduction into one area of finance: "portfolio theory" using R. A second part will then concentrate on the Capital-Asset-Pricing-Method (CAPM) and its assumptions, implications and drawbacks. Note: All code that is needed for the simulations, data…
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Google to Developers: Here’s How to Stop Making Dumb Chatbots

Google to Developers: Here’s How to Stop Making Dumb Chatbots | Data is big | Scoop.it
The search company is releasing the secret sauce it uses to make sense of ambiguous language. It could help developers build apps that actually understand us.
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How to Rank 10% in Your First Kaggle Competition

How to Rank 10% in Your First Kaggle Competition | Data is big | Scoop.it

Kaggle is the best place for learning from other data scientists. Many companies provide data and prize money to set up data science competitions on Kaggle. Recently I had my first shot on

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Artistic Style Transfer for Videos | GitXiv

Artistic Style Transfer for Videos | GitXiv | Data is big | Scoop.it
We present an approach that transfers the style from one image (for example, a painting) to a whole video sequence. We make use of recent advances in style transfer in still images and propose new initializations and loss functions applicable to videos.
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We present an approach that transfers the style from one image (for example, a painting) to a whole video sequence.
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Artificial Neural Networks in NetLogo 

Artificial Neural Networks in NetLogo  | Data is big | Scoop.it

As a way to continue with AI algorithms implemented in NetLogo, in this post we will see how we can make a simple model to investigate about Artificial Neural Networks (ANN). 

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Artificial Intelligence - foundations of computational agents

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This book is published by Cambridge University Press, 2010.

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Cluster analysis in R: determine the optimal number of clusters - Stack Overflow

Cluster analysis in R: determine the optimal number of clusters - Stack Overflow | Data is big | Scoop.it

Being a newbie in R, I'm not very sure how to choose the best number of clusters to do a k-means analysis. After plotting a subset of below data, how many clusters will be appropriate? How can I perform cluster dendro analysis?

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Very detailed answer with practically applicable examples

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Why I use ggplot2

Why I use ggplot2 | Data is big | Scoop.it
If you’ve read my blog, taken one of my classes, or sat next to me on an airplane, you probably know I’m a big fan of Hadley Wickham’s ggplot2 package, especially compared to base R plotting.
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Explaining “Deep Learning” to a 5th Grader!

Explaining “Deep Learning” to a 5th Grader! | Data is big | Scoop.it
Oops! I got into the mess again. My younger son is showing all the signs of the “Gen not decipherable”, this time he saw me researching about “Deep Learning” and asked the dreaded question again –
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Building a Deep Learning (Dream) Machine

Building a Deep Learning (Dream) Machine | Data is big | Scoop.it
Some pointers on the slippery path towards building your own machine for Deep Learning
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A Collection of Winograd Schemas

A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution. The schema takes its name from a well-known example by Terry Winograd (1972)
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A Winograd schema is a pair of sentences that differ in only one or two words and that contain an ambiguity that is resolved in opposite ways in the two sentences and requires the use of world knowledge and reasoning for its resolution.
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Sketch Simplification

Sketch Simplification | Data is big | Scoop.it

We present a novel technique to simplify sketch drawings based on learning a series of convolution operators. In contrast to existing approaches that require vector images as input, we allow the more general and challenging input of rough raster sketches such as those obtained from scanning pencil sketches. We convert the rough sketch into a simplified version which is then amendable for vectorization. This is all done in a fully automatic way without user intervention. Our model consists of a fully convolutional neural network which, unlike most existing convolutional neural networks, is able to process images of any dimensions and aspect ratio as input, and outputs a simplified sketch which has the same dimensions as the input image.

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OpenAI Gym tutorial (Python)

OpenAI Gym tutorial (Python) | Data is big | Scoop.it
OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms.
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OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms.
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What kind of decision boundaries does Deep Learning (Deep Belief Net) draw? Practice with R and {h2o} package - Data Scientist TJO in Tokyo

What kind of decision boundaries does Deep Learning (Deep Belief Net) draw? Practice with R and {h2o} package - Data Scientist TJO in Tokyo | Data is big | Scoop.it
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R for Deep Learning (I): Build Fully Connected Neural Network from Scratch – ParallelR

R for Deep Learning (I): Build Fully Connected Neural Network from Scratch – ParallelR | Data is big | Scoop.it
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How I build up a ggplot2 figure

How I build up a ggplot2 figure | Data is big | Scoop.it

Recently, Jeff Leek at Simply Statistics discussed why he does not use ggplot2. He notes “The bottom line is for production graphics, any system requires work.” and describes a default plot that needs some work

 

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