Bias Variance Decompositions using XGBoost
This blog dives into a theoretical machine learning concept called the bias-variance decomposition. This decomposition is a method which examines the expected generalization error for a given learning algorithm and a given data source. This helps us understand questions like: – How can I achieve higher... Read more
Taking Your Machine Learning from 0 to 10
Madhura Dudhgaonkar is the senior director of Machine Learning at Workday Inc. She believes that it’s possible to deploy machine learning within your enterprise, but it takes a few steps to get exactly right. She loves to get into unknowns and things we haven’t tried yet,... Read more
ODSC Meetup: Automated and Interpretable Machine Learning
Last week, ODSC hosted a talk by Dr. Francesca Lazzeri, Senior Machine Learning Scientist at Microsoft, on the capabilities of automated and interpretable machine learning software in Microsoft’s Azure. Notably, this talk is part of a series that covers a variety of data science topics. The... Read more
When Less is More: A Brief Story About Feature Engineering with XGBoost
I played a minor role launching RAPIDS on Google Dataproc by refining a model that predicts taxi fare in New York City. Geographic location of passenger pick-ups and drops-offs were columns in the data. These are recorded as longitude and latitude measurements, with precision to many decimal places.... Read more
Watch: Effective Transfer Learning for NLP
Transfer learning, the practice of applying knowledge gained on one machine learning task to aid the solution of a second task, has seen historic success in the field of computer vision. The output representations of generic image classification models trained on ImageNet have been leveraged to... Read more
Watch: The Future of Machine Learning
See the video from Accelerate AI West 2019 where keynote, Alex Holub, talks about where the biggest innovations in applied Machine Learning will occur in the next 5 years. He is discussing how some of the largest global organizations are using Machine Learning today, and the... Read more
ML Operationalization: From What and Why? to How and Who?
Operationalization may be the newest 18 letter word in AI, but there are specific steps to removing your AI initiative from the silos and putting it into production at scale. Sivan Metzger of ParallelM is here to share his experiences, mistakes and all, deploying machine learning... Read more
Interpretable Machine Learning – Fairness, Accountability, and Transparency in ML systems
Editor’s note: Sayak is a speaker for ODSC West in San Francisco this November! Be sure to check out his talk, “Interpretable Machine Learning – Fairness, Accountability and Transparency in ML systems,” there! The problem is it is much harder to evaluate machine learning systems than... Read more
Watch: Introduction to Reinforcement Learning
Reinforcement Learning (RL), the field of sequential decision making, has evolved significantly within the last few years, achieving super-human performance in solving complex board games, 2D Atari and 3D games (Doom, Quake, StarCraft). But this is not just about games, this is about solving arbitrary problems... Read more
The Past, Present, and Future of Automated Machine Learning
As a consultant in data science and machine learning, and also a tech journalist, I’m in a position to recognize current trends in the industry. One of the latest crazes centers around “automated machine learning” or AutoML as many call it. In fact, I’ve written a... Read more