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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Automated and Unmysterious Machine Learning in Cancer Detection

Automated and Unmysterious Machine Learning in Cancer Detection | Data is big | Scoop.it
explanation_wrong <- explain(
test_wrong[1:6, -1],
explainer = explainer,
n_labels = 1,
n_features = 5,
kernel_width = 0.5)

plot_features(explanation_wrong)
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Making Predictions over HTTP with R

Making Predictions over HTTP with R | Data is big | Scoop.it
Using R as a Production Machine Learning Language (Part I) Why is this necessary? Getting Started with Plumber on a trained model Prepping Data Train Model Evaluating Model Save model to RDS Building Plumber API Plumber API files First use of Plumber Annotation - Health check endpoint Landing Page Prediction Endpoint Putting it all together Concluding Remarks Using R as a Production Machine Learning Language (Part I) There’s often confusion amoung the data science and machine learning crowd about the quality of R as a production level language for deploying predictive models.
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Top 50 ggplot2 Visualizations - The Master List (With Full R Code)

Top 50 ggplot2 Visualizations - The Master List (With Full R Code) | Data is big | Scoop.it
R Language Tutorials for Advanced Statistics
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Customer Churn – Logistic Regression with R

Customer Churn – Logistic Regression with R | Data is big | Scoop.it
In the customer management lifecycle, customer churn refers to a decision made by the customer about ending the business relationship. It is also referred as loss of clients or customers. Customer loyalty and customer churn always add up to 100%. If a firm has a 60% of loyalty rate, then their loss or churn rate of customers is 40%. As per 80/20 customer profitability rule, 20% of customers are generating 80% of revenue.

Via Eric Feuilleaubois
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Deep Learning with R

Deep Learning with R | Data is big | Scoop.it
For R users, there hasn’t been a production grade solution for deep learning (sorry MXNET). This post introduces the Keras interface for R and how it can be used to
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Tensorflow and Keras finally find their way to the R world
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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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Share your insight
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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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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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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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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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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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Using TensorFlow with R

Using TensorFlow with R | Data is big | Scoop.it
TensorFlow
TensorFlow™ is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for the purposes of conducting machine learning and deep neural networks research, but the system is general enough to be applicable in a wide variety of other domains as well.
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Using TensorFlow with R The TensorFlow API is composed of a set of Python modules that enable constructing and executing TensorFlow graphs. The tensorflow package provides access to the complete TensorFlow API from within R. Here’s a simple example of making up some data in two dimensions and then fitting a line to it:
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Creating Interactive data visualization using Shiny App in R (with examples)

Creating Interactive data visualization using Shiny App in R (with examples) | Data is big | Scoop.it
Introduction to Shiny App & detailed explaination in UI.R & Server.R along with codes, hands on exercise with Loan Prediction practice problem
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Feature selection with the Boruta Algorithm

Feature selection with the Boruta Algorithm | Data is big | Scoop.it
One of the most important steps in building a statistical model is deciding which data to include. With very large datasets and models that have a high computational cost, impressive efficiency can be realized by identifying the most (and least) useful features of a dataset prior to running a model. Feature selection is the process…
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Top 15 Evaluation Metrics for Classification Models

Top 15 Evaluation Metrics for Classification Models | Data is big | Scoop.it
Computing just the accuracy to evaluate a classification model is not enough. This tutorial shows how to build and interpret the evaluation metrics.
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Principal Component Analysis Course Using FactoMineR - Articles - STHDA

Principal Component Analysis Course Using FactoMineR - Articles - STHDA | Data is big | Scoop.it
Statistical tools for data analysis and visualization
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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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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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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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