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... Read more
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... Read more
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... Read more
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... Read more
This is a two-part series about using machine learning to hack my taste in music. In this first piece, I applied unsupervised learning techniques and tools on Pandora data to analyze songs that I like. The second part, which will be published soon, is about using supervised on Spotify data to... Read more
In this interview, Jonathan Schwarz of Google DeepMind shares insight on Deep Learning projects. He offers tips and advice for the those interested in DL, and explains whether DL projects relate to other data driven projects? He comments on effective team size, software, frameworks, common mistakes, resources for learning, and more all under 30... Read more
On word embeddings – Part 2: Approximating the Softmax
Table of contents: Softmax-based Approaches Hierarchical Softmax Differentiated Softmax CNN-Softmax Sampling-based Approaches Importance Sampling Adaptive Importance Sampling Target Sampling Noise Contrastive Estimation Negative Sampling Self-Normalisation Infrequent Normalisation Other Approaches Which Approach to Choose? Conclusion This is the second post in a series on word embeddings and representation learning. In... Read more
Cognitive Machine Learning (2): Uncertain Thoughts
She pined in thought,  And with a green and yellow melancholy She sat like Patience on a monument,  Smiling at grief. Was not this love indeed?   In King Lear, Shakespeare stirs a sense of self-consciousness by invoking Patience, sitting up high; isolated in her... Read more
I was reading yet another blog post titled “Why our team moved from to ” (I forgot which one) and I started wondering if you can generalize it a bit. Is it possible to generate a N * N contingency table of moving from language X to language Y?... Read more
How NOT to program the TensorFlow Graph
Using TensorFlow from Python is like using Python to program another computer. Some Python statements build your TensorFlow program, some Python statements execute that program, and of course some Python statements aren’t involved with TensorFlow at all. Being thoughtful about the graphs you construct can help you avoid confusion... Read more