Data is big
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Machine Learning Online Courses

The complete list of online courses from top universities (Stanford, MIT, Harvard), MOOC (Coursera, Udacity, edX), and other online learning companies
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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 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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An Analysis of Deep Neural Network Models for Practical Applications

Abstract: Since the emergence of Deep Neural Networks (DNNs) as a prominent technique in the field of computer vision, the ImageNet classification challenge has played a major role in advancing the state-of-the-art. While accuracy figures have steadily increased, the resource utilisation of winning models has not been properly taken into account. In this work, we present a comprehensive analysis of important metrics in practical applications: accuracy, memory footprint, parameters, operations count, inference time and power consumption. Key findings are: (1) power consumption is independent of batch size and architecture; (2) accuracy and inference time are in a hyperbolic relationship; (3) energy constraint are an upper bound on the maximum achievable accuracy and model complexity; (4) the number of operations is a reliable estimate of the inference time. We believe our analysis provides a compelling set of information that helps design and engineer efficient DNNs.
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Compressing and regularizing deep neural networks

Compressing and regularizing deep neural networks | Data is big | Scoop.it
Improving prediction accuracy using deep compression and DSD training.
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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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RStudio IDE Easy Tricks You Might've Missed

RStudio IDE Easy Tricks You Might've Missed | Data is big | Scoop.it
by Sean Lopp

The RStudio IDE reached version 1.0 this month. The IDE has come a long way since the initial release 5 and a half years ago. Many major features have been built: projects, package building tools, notebooks. During that same period, often hidden in the shadows, a growing list of smaller features has been
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