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 January 23:
✓Raspberry Pi or C64 Mini for free
✓Save up to $330
✓ Group Discount
Register Now
Join the ML Revolution!
Register until January 23:
✓Raspberry Pi or C64 Mini for free
✓Save up to $330
✓ Group Discount
Register Now
Join the ML Revolution!
Register until March 5:
✓ML Intro Day for free
✓Save up to 500 €
✓10 % Team Discount
Register Now
Join the ML Revolution!
Register until March 5:
✓ML Intro Day for free
✓Save up to 500 €
✓10 % Team Discount
Register Now
Join the ML Revolution!
Until Conference starts:
✓Special discount for Freelancers
✓10% Team Discount
Register Now
Join the ML Revolution!
Until Conference starts:
✓Special discount for Freelancers
✓10% Team Discount
Register Now

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.

Behind the Tracks