Extreme Gradient Boosting with R

Extreme Gradient Boosting is among the hottest libraries in supervised machine learning these days. It supports various objective functions, including regression, classification, and ranking. It has gained much popularity and attention recently as it was the algorithm of choice for many winning teams of a number of machine learning competitions.

Jupyter notebooks and tensorboard on Polyaxon

We’re introducing a new feature at Polyaxon to help you iterate on your projects and share your work; Jupyter Notebook and Tensorboard.

How Biased AI is Holding Us Back, and Two Things We Can Do About it

From the largest and most successful tech corporations to the smallest start-ups just finding their footing, most will agree that increasing diversity is in the best interests of customers, employees, and the general public. However, we in the tech world often fail to recognize the impact of our own biases. We sometimes think that, because our products and services are based on 0’s and 1’s, everything we put out into the world is fair and logical. Not true! This International Women’s Day (Thursday), let’s take a closer look at the biases that inhabit so much of our work, as well as some of the ways we can work toward a culture of inclusive AI.

Deep Misconceptions About Deep Learning

I started this article with the hopes of confronting a few misconceptions about Deep Learning (DL), a field of Machine Learning that is simultaneously labelled a silver bullet and research hype. The truth lies somewhere in the middle, and I hope I can un-muddy the waters?—?at least a little bit. Importantly, I hope to clarify some processes to attack DL problems and also discuss why it performs so well in some areas such as Natural Language Processing (NLP), image recognition, and machine-translation while failing at others.

Semiparametric Regression in R

In the context of evaluating relationships between one or more target variables and a set of explanatory variables, semiparametric regression is one such technique that provides the user with some flexibility in modeling complex data without maintaining stringent assumptions. With semiparametric regression, the goal is to develop a properly specified model that integrates the simplicity of parametric estimation with the flexibility provided by nonparametric splines.