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Modeling
Choroplethr v3.6.0 is now on CRAN

Choroplethr v3.6.0 is now on CRAN

Data VisualizationModelingRToolsTools & Languagesposted by Ari Lamstein May 9, 2017

Choroplethr version 3.6.0 is now on CRAN. This version adds functionality for getting and mapping demographics of US Census Tracts. You can install it from the R console as follows: 1 2 3 install.packages("choroplethr") packageVersion("choroplethr") ‘3.6.0’ To use this functionality you will need an API... Read more

Introduction to Natural Language Processing with NLTK

ModelingNLP/Text AnalyticsPythonTools & LanguagesBeginner|Intro|Natural Language Processing|NLTK|text analyticsposted by Sukanya Mandal May 7, 2017

Hello all and welcome to the second of the series – NLP with NLTK. The first of the series can be found here, incase you have missed. In this article we will talk about basic NLP concepts and use NLTK to implement the concepts. Contents: Corpus... Read more
An overview of gradient descent optimization algorithms

An overview of gradient descent optimization algorithms

ModelingPredictive Analyticsposted by Sebastian Ruder April 27, 2017

Note: If you are looking for a review paper, this blog post is also available as an article on arXiv. Table of contents: Gradient descent variants Batch gradient descent Stochastic gradient descent Mini-batch gradient descent Challenges Gradient descent optimization algorithms Momentum Nesterov accelerated gradient Adagrad Adadelta... Read more
Random-Walk Bayesian Deep Networks: Dealing with Non-Stationary Data

Random-Walk Bayesian Deep Networks: Dealing with Non-Stationary Data

Deep LearningModelingposted by Thomas Wiecki, PhD April 27, 2017

Thomas originally posted this article here at http://twiecki.github.io  Most problems solved by Deep Learning are stationary. A cat is always a cat. The rules of Go have remained stable for 2,500 years, and will likely stay that way. However, what if the world around you is changing? This... Read more
NYC Pre-K Explorer

NYC Pre-K Explorer

Data VisualizationModelingResearchposted by NYC Data Science Academy April 26, 2017

Shiny Project contributed by Amy Tzu-Yu Chen – Data Science Student in the NYC Data Science Academy Bootcamp Motivation In 2013, Mayor De Blasio campaigned on a promise of universal pre-kindergarten. The program makes access to free pre-kindergarten education available to all NYC families, regardless of child’s abilities and... Read more
Dealing with arrays which are bigger than memory – an introduction to biggus

Dealing with arrays which are bigger than memory – an introduction to biggus

Data WranglingModelingPythonTools & Languagesposted by Philip Elson April 25, 2017

I often deal with huge gridded datasets which either stretch or indeed are beyond the limits of my computer’s memory. In the past I’ve implemented a couple of workarounds to help me handle this data to extract meaningful analyses from them. One of the most intuitive... Read more
Exploring the Relationship between Religion and Demographics in R

Exploring the Relationship between Religion and Demographics in R

Data VisualizationModelingRStatisticsTools & Languagesposted by Julia Silge April 25, 2017

Today’s guest post is by Julia Silge. Take a look at her work on (“Mapping US Religion Adherence by County in R“) where she demonstrated how to work with US religion adherence data in R. In this post she explores the relationship between that dataset and US... Read more
9 ways to Level up your Data Science practice

9 ways to Level up your Data Science practice

Data VisualizationData WranglingModelingPredictive AnalyticsToolsTools & LanguagesLearning data scienceposted by Domino Data Lab April 24, 2017

We love reading articles with tips and best practices, and we agree with a lot of the advice we see out there (#5 on this list is great!). So, we asked the Domino team for advice to pass on to researchers and scientists searching for ways to get to... Read more
TensorFlow Clusters: Questions and Code

TensorFlow Clusters: Questions and Code

Deep LearningModelingPlatformsTools & Languagesposted by Aaron Schumacher April 23, 2017

One way to think about TensorFlow is as a framework for distributed computing. I’ve suggested that TensorFlow is a distributed virtual machine. As such, it offers a lot of flexibility. TensorFlow also suggests some conventions that make writing programs for distributed computation tractable. When is there... Read more
New notebooks for Think Stats

New notebooks for Think Stats

ModelingPythonStatisticsTools & LanguagesStatisticsposted by Allen Downey April 22, 2017

Getting ready to teach Data Science in the spring, I am going back through Think Stats and updating the Jupyter notebooks.  When I am done, each chapter will have a notebook that shows the examples from the book along with some small exercises, with more substantial... Read more
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