By now, even remote villages on uncharted islands in the Pacific know that the U.S. is in the midst of a protracted partial government shutdown. It’s having real impacts on the lives of Federal government workers but they aren’t the only ones. Much of the interaction Federal agencies have with the populace takes place online and the gateway to most of these services/information is a web site.

Why vitae? The process of maintaining a CV can be tedious. It’s a task I often forget about – that is until someone requests it and I find that my latest is woefully out of date. To make matters worse, these professional updates often need repeating across variety of sites (such as ORCID and LinkedIn).

PyOD: A Python Toolbox for Scalable Outlier Detection

In this post we will return to the Pitchfork music review data, parts of which I’ve analyzed in previous posts. Our goal here will be to use text mining and natural language processing (NLP) to understand linguistic signals of album quality. This type of analysis helps us understand what Pitchfork reviewers appreciate or dislike, and gives us a sense of the criteria which distinguish good albums from bad ones. We will use the R package Quanteda, developed by Ken Benoit and colleagues, to do the text mining and NLP. We will use the glmnet package to build a LASSO regression model to predict the album review score from the review text.

By Preet Gandhi, NYU

Welcome to the second installment of our University Club winner interviews!**

I will be uploading a series of presentations on ‘Elements of Neural Networks and Deep Learning’. In these video presentations I discuss the derivations of L -Layer Deep Learning Networks, starting from the basics. The corresponding implementations are available in vectorized R, Python and Octave are available in my book ‘Deep Learning from first principles:Second edition- In vectorized Python, R and Octave‘

Deep Learning Summit, San Francisco, Jan 24 - 25

Amazon SageMaker is a complete machine learning (ML) workflow service for developing, training, and deploying models, lowering the cost of building solutions, and increasing the productivity of data science teams. Amazon SageMaker comes with many predefined algorithms. You can also create your own algorithms by supplying Docker images, a training image to train your model and an inference model to deploy to a REST endpoint.

And our noontime competition continues . . .