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
The Best Machine Learning Research of June 2019
Machine Learning and the data science industry is always changing. To keep you updated on the most recent discoveries, we’ve compiled the 5 most exciting machine learning research pieces that expand what we thought we knew about machine learning and the industries to which it relates. ... Read more
A Manager’s Guide to Starting a Computer Vision Program
So you’re thinking of starting a computer vision program, but you’ve realized now that the logistics are overwhelming. What framework do you use? What infrastructure? Do you go with an out of the box solution or take the time to build your own? Cloud GPU or... Read more
Best Practices for Deploying Machine Learning in the Enterprise
This post discusses the best practices for deploying machine learning in the enterprise. If you’re an organization worried about being left behind with deploying machine learning, it’s not just you. According to Gartner’s Hype Cycle Chart, machine (and deep) learning are the biggest hyped trends of... Read more
An Introduction to Active Learning
The current utility and accessibility of machine learning is in part due to the exponential increase in the availability of data over time. While data is abundant, labels that are required for specific supervised machine learning tasks can be difficult to obtain. At ODSC West in... Read more