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An overview of gradient descent optimization algorithms

An overview of gradient descent optimization algorithms

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 ...

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

Random-Walk Bayesian Deep Networks: Dealing with Non-Stationary D...

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 is common, for example when applying ...

NYC Pre-K Explorer

NYC Pre-K Explorer

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 family income. ...

Exploring the Relationship between Religion and Demographics in R

Exploring the Relationship between Religion and Demographics in R

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 Demographic data. I started exploring the ASARB religion census in my previous guest ...

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

Dealing with arrays which are bigger than memory – an intro...

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 ways of reducing gridded datasets is through indexing/slicing and in ...

9 ways to Level up your Data Science practice

9 ways to Level up your Data Science practice

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 that next level, and here’s what we heard:   1. Learn ways to parallelize your code. You ...

Fixing an infelicity in ‘leaps’

Fixing an infelicity in ‘leaps’

The ‘leaps’ package for R is ancient – this is its tenth twentieth year on CRAN.  It uses old Fortran code by the Australian computational statistician Alan Miller. The Fortran 90 versions are on the web, but Fortran 90 compilation with R wasn’t portable back then, so I used the older Fortran 77 version. The main point back in 1997 was to ...

TensorFlow Clusters: Questions and Code

TensorFlow Clusters: Questions and Code

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 a cluster? A Hadoop or Spark cluster is ...

New notebooks for Think Stats

New notebooks for Think Stats

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 exercises at the end. If you are reading the book, you can get the ...