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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
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
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
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
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
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
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
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
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
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