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Using the ‘What-If Tool’ to Investigate Machine Learning Models
In this era of explainable and interpretable Machine Learning, one merely cannot be content with simply training the model and obtaining predictions from it. To be able to really make an impact and obtain good results, we should also be able to probe and investigate our... Read more
Visualizing Machine Learning Datasets with Google’s FACETS
There has been a lot of uproar as to how a large quantity of training data can have a tremendous impact on the results of a machine learning model. However, along with data quantity, it is also the quality that is critical to building a powerful... Read more
A Concrete Application of Topological Data Analysis
Today, I will present a Machine Learning application of Topological Data Analysis (TDA), a rapidly evolving field of data science that makes use of topology to improve data analysis. It is largely inspired by one of my projects. Great! Wait… what is TDA? I will start by briefly... Read more
cuSpatial Accelerates Geospatial and Spatiotemporal Processing
The Internet of Things (IOT) has spawned explosive growth in sensor data. Location is some of the most important information generated by sensors, and dynamic location is vital in the case of mobile sensors. Examples include: mobile phones (GPS), vehicles, robots, and cameras. [Related Article: The... Read more
What Your Business Needs to Know About Data Variety
The bigger your business, the more likely your data is…interesting. Old methods of data collection put data in silos across several departments. The computing power to handle data in all its forms wasn’t there, so breaking it up in the name of agile operations was key.... Read more
ML and Behavioral Economics for Personalized Choice Architecture
Personalized choice architecture is a new and interesting field surrounding the idea that we can predict and influence consumers’ choices, ensuring that they get the best product or best outcome possible. In a recent paper by Emir Hrnjic and Nikodem Tomczak (researchers at NUS Business School... Read more
Machine Learning Model Fairness in Practice
Editor’s Note: See Jakub’s talk about Machine Learning “Model Fairness in Practice” at ODSC West 2019 In the last few years, the interest around fairness in machine learning has been gaining a lot of momentum. Rightfully so: our models are becoming more and more prevalent in... Read more
The Best Machine Learning Research of Summer 2019
Academic institutions, AI labs, and research departments of other organizations are constantly generating novel insights into data science, whether it’s machine learning, deep learning, NLP, or other disciplines. Summer 2019 generated some interesting machine learning research, and here are a few of our top picks. [Related... Read more
FSGAN: Subject Agnostic Face Swapping and Reenactment
Generative adversarial networks are getting a lot of press as the general public raises their fears on the forgery techniques, but the data science community has been tracking their development, precision, benefits, and threats for years. Recently though, the team of Yuval Nirkin, Yosi Keller (both... Read more
The History and Future of Machine Learning at Reddit
Let’s take a walk through the history of machine learning at Reddit from its original days in 2006 to where we are today, including the pitfalls and mistakes made as well as their current ML projects and future efforts in the space. Based on a talk... Read more