Data is big
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R: The most powerful and most widely used statistical software

In the last ten years, the open source R statistics language has exploded in popularity and functionality, emerging as the data scientist's tool of choice.

 

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Data is big
"The future is here. It's just not evenly distributed yet." - 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 MMC Ventures AI Investment Framework: 17 success factors for the age of AI

The MMC Ventures AI Investment Framework: 17 success factors for the age of AI | Data is big | Scoop.it

Artificial intelligence — specifically, machine learning (ML) — is a powerful ‘enabling technology’ that represents a paradigm shift in software capability.

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The 5 Levels of Machine Learning Iteration

The 5 Levels of Machine Learning Iteration | Data is big | Scoop.it
Practical machine learning has a distinct cyclical nature that demands constant iteration, tuning, and improvement. We aim to showcase its beauty.
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Everything that Works Works Because it's Bayesian: Why Deep Nets Generalize?

Everything that Works Works Because it's  Bayesian: Why Deep Nets Generalize? | Data is big | Scoop.it
The Bayesian community should really start going to ICLR. They really should have started going years ago. Some people actually have. For too long we Bayesians have, quite arrogantly, dismissed deep neural networks as unprincipled, dumb black boxes that lack elegance. We said that highly over-parametrised models fitted via maximum
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Creating a Modern OCR Pipeline Using Computer Vision and Deep Learning

In this post we will take you behind the scenes on how we built a state-of-the-art Optical Character Recognition (OCR) pipeline for our mobile document scanner.
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Robotics Institute Seminar Series - YouTube

Robotics Institute Seminar Series - YouTube | Data is big | Scoop.it
A seminar series hosted by Carnegie Mellon University's Robotics Institute.
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A seminar series hosted by Carnegie Mellon University's Robotics Institute.
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ReinforcementLearning: A package for replicating human behavior in R

ReinforcementLearning: A package for replicating human behavior in R | Data is big | Scoop.it
Nicolas Proellochs and Stefan Feuerriegel 2017-04-06 Introduction Reinforcement learning has recently gained a great deal of traction in studies that call for
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Explained Visually

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Explained Visually (EV) is an experiment in making hard ideas intuitive inspired the work of Bret Victor's Explorable Explanations.
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Unpacking Assignment %<-% 

The zeallot package defines an operator for unpacking assignment, sometimes called parallel assignment or destructuring assignment in other programming languages. The operator is written as %<-% and used like this.

{ lat : lng } %<-% list(38.061944, -122.643889)
The result is that the list is unpacked into its elements, and the elements are assigned to lat and lng.
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The most comprehensive Data Science learning plan for 2017

This article features a year long learning path for aspiring data scientist, intermediate & transitioner to progress in data science industry for R & Python
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How to find daily good deals online, automatically with R?

How to find daily good deals online, automatically with R? | Data is big | Scoop.it
As defined here, “a data scientist is someone who is better at statistics than any software engineer and better at software engineering than any statistician.” Therefore, this blog post focuses on…
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Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour | Data is big | Scoop.it
In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization.
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How long does it take to train the state-of-the-art imagenet model? The answer is one hour :) 

Yeah, remember when we just started with AlexNet it took a week, and our model has already grown like 10x bigger in the meantime too! So consider the "software Moore's Law" broken.
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Deep Learning Challenges from a Kaggle Competition

Vladimir Iglovikov, Kaggle Master, talks about a Deep Learning approach to the "Dstl Satellite Imagery Feature Detection" competition, challenges an
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Machine learning 10 - Funny pictures

Machine learning 10 - Funny pictures | Data is big | Scoop.it
The following are funny pictures related to machine learning or data science I found online. It is a great way to learn some concepts i
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[1206.4634] Artist Agent: A Reinforcement Learning Approach to Automatic Stroke Generation in Oriental Ink Painting

Oriental ink painting, called Sumi-e, is one of the most appealing painting styles that has attracted artists around the world. Major challenges in computer-based Sumi-e simulation are to abstract complex scene information and draw smooth and natural brush strokes. To automatically find such strokes, we propose to model the brush as a reinforcement learning agent, and learn desired brush-trajectories by maximizing the sum of rewards in the policy search framework. We also provide elaborate design of actions, states, and rewards tailored for a Sumi-e agent. The effectiveness of our proposed approach is demonstrated through simulated Sumi-e experiments.
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AudioSet

A large-scale dataset of manually annotated audio eventsAudioSet consists of an expanding ontology of 632 audio event classes and a collection of 2,084,320 human-labeled 10-second sound clips drawn from YouTube videos. The ontology is specified as a hierarchical graph of event categories, covering a wide range of human and animal sounds, musical instruments and genres, and common everyday environmental sounds. By releasing AudioSet, we hope to provide a common, realistic-scale evaluation task for audio event detection, as well as a starting point for a comprehensive vocabulary of sound events.
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A large-scale dataset of manually annotated audio events AudioSet consists of an expanding ontology of 632 audio event classes and a collection of 2,084,320 human-labeled 10-second sound clips drawn from YouTube videos.
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Explaining the decisions of machine learning algorithms | StatsBlogs.com | All About Statistics

Explaining the decisions of machine learning algorithms | StatsBlogs.com | All About Statistics | Data is big | Scoop.it
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Machine Learning Boot Camp - live and archived videos Jan. 23 – Jan. 27, 2017

Machine Learning Boot Camp - live and archived videos Jan. 23 – Jan. 27, 2017 | Data is big | Scoop.it
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Machine Learning Boot Camp Jan. 23 – Jan. 27, 2017
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Introduction to Forecasting with ARIMA in R

Data Scientist Ruslana Dalinina explains how to forecast demand with ARIMA in R. Learn how to fit, evaluate, and iterate an ARIMA model with this tutorial.
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46 Questions on SQL to test a data science professional (Skilltest Solution)

This article features 46 questions on SQL every data science professional should know. Questions related to DDL, DML, Joins, Update, Drop, where, Groupby
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