The Conference for Machine Learning Innovation

Doing More with Less: Building Machine Learning Solutions without Large Labeled Datasets

Session
Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until October 20:
✓ Save up to $233
✓ Team discount
✓ Extra Specials for Freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Register until November 03:
✓ Save up to €494
✓ 10% Team Discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Join the ML Revolution!
Until the Conference starts:
✓ Group discount
✓ Special discount for freelancers
Register Now
Infos

Data is the fuel behind machine learning solutions but also its biggest weakness. The dependency on large labeled datasets makes many machine learning processes completely unpractical. How can organizations address this challenge? This session presents a series of techniques that can help companies build machine learning solutions in the absence of large labeled datasets. Exploring methods such as semi-supervised, weakly-supervised or reinforcement learning to different privacy techniques, we explore patterns and neural network architectures that work efficiently in scenarios without large labeled datasets. To keep things practical we will discuss several case studies that illustrate how these methods are being used in real-world machine learning solutions today.

This Session Diese Session Take me to the current program of . Hier geht es zum aktuellen Programm von Singapore Singapore , Berlin Berlin or oder Munich Munich .

Behind the Tracks