Data is big
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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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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
ukituki's insight:
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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Making an R based ML model accessible through a simple API

Building an accurate machine learning (ML) model is a feat on its own. But once you’re there, you still need to find a way to make the model accessible t
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Analyzing Genomics Data at Scale using R, AWS Lambda, and Amazon API Gateway | AWS Compute Blog

Analyzing Genomics Data at Scale using R, AWS Lambda, and Amazon API Gateway | AWS Compute Blog | Data is big | Scoop.it
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Learning Reinforcement Learning (With Code, Exercises and Solutions) | Open Data Science

Skip all the talk and go directly to the Github Repo with code and exercises. WHY STUDY REINFORCEMENT LEARNING Reinforcement Learning is one of the fields I’m
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What I Learned Recreating One Chart Using 24 Tools - Features - Source: An OpenNews project

What I Learned Recreating One Chart Using 24 Tools - Features - Source: An OpenNews project | Data is big | Scoop.it
Source - Journalism Code, Context & Community
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50 things I learned at NIPS 2016 – Ought

50 things I learned at NIPS 2016 – Ought | Data is big | Scoop.it
I learned many things about AI and machine learning at the NIPS 2016 conference. Here are a few that are particularly suited to being communicated in the space of a few sentences.
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Introducing the Data Science Maturity Model

Introducing the Data Science Maturity Model | Data is big | Scoop.it
The Data Science Maturity Model helps leaders and practitioners identify existing gaps and direct investment in their data science programs.
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R for SQListas (1): Welcome to the Tidyverse

R for SQListas (1): Welcome to the Tidyverse | Data is big | Scoop.it
R for SQListas, what's that about? This is the 2-part blog version of a talk I've given at DOAG Conference this week. I've also uploaded the slides (no ppt; just pretty R presentation ;-) ) to the articles section, but if you'd like a little text I'm encouraging you to read on. That is, if…
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Run compiled R packages in AzureML

Run compiled R packages in AzureML | Data is big | Scoop.it
We've shown a few times here how you can run R code on data in the cloud with Azure ML Studio, and even how to enable that code as a web service to be called from other applications. But what if you want to run code in a compiled language, like C++?
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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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Deep Learning Gallery - a curated list of awesome deep learning projects

Deep Learning Gallery - a curated list of awesome deep learning projects | Data is big | Scoop.it
Deep Learning Gallery - a curated list of awesome deep learning projects
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A Guide to Deep Learning by YerevaNN

Deep learning is a fast-changing field at the intersection of computer science and mathematics. It is a relatively new branch of a wider field called machine learning.
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Daniel Oratokhai's curator insight, January 2, 5:35 AM

A Guide to Deep Learning by YerevaNN

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Practical Deep Learning For Coders—18 hours of lessons for free

Practical Deep Learning For Coders—18 hours of lessons for free | Data is big | Scoop.it
Welcome to fast.ai's 7 week course, "Practical Deep Learning For Coders, Part 1", taught by Jeremy Howard (Kaggle's #1 competitor 2 years running, and founder of Enlitic). Learn how to build state of the art models without needing graduate-level math—but also without dumbing anything down. Oh and one other thing... it's totally free!
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Daniel Oratokhai's curator insight, January 3, 1:01 AM

Practical Deep Learning For Coders—18 hours of lessons for free

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State of the art deep learning model for question answering

State of the art deep learning model for question answering | Data is big | Scoop.it
We introduce the Dynamic Coattention Network, a state of the art question answering deep learning model that significantly outperforms all existing systems on the Stanford Question Answering dataset.
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The major advancements in Deep Learning in 2016 - Tryolabs Blog

Deep Learning has been the core topic in the Machine Learning community the last couple of years and 2016 was not the exception. In this article, we will go through the advancements we think have contributed the most (or have the potential) to move the field forward and how organizations and the community are making sure that these powerful technologies are going to be used in a way that is beneficial for all.
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Embed, encode, attend, predict: The new deep learning formula for state-of-the-art NLP models

Over the last six months, a powerful new neural network playbook has come together for Natural Language Processing. The new approach can be summarised as a simple four-step formula: embed, encode, attend, predict. This post explains the components of this new approach, and shows how they're put together in two recent systems.
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