The Conference for Machine Learning Innovation

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

Session
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✓Save up to 223 €
✓10 % Team Discount
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Register until December 12:
✓ML Intro Day for free
✓Raspberry Pi or C64 Mini for free
✓Save up to $580
Register Now
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Register until November 7th:
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✓10% Team Discount
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Register until November 7th:
✓Save up to € 210
✓10% Team Discount
Register Now
Infos
Thursday, September 10 2020
09:00 - 09:45

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 belongs to the Diese Session gehört zum Programm vom SingaporeSingapore program. Take me to the program of . Hier geht es zum Programm von Online Edition Online Edition .

This Session belongs to the Diese Session gehört zum Programm vom SingaporeSingapore program. Take me to the program of . Hier geht es zum Programm von Munich Munich .

Take me to the full program of Zum vollständigen Programm von Singapore Singapore .

This Session belongs to the Diese Session gehört zum Programm vom SingaporeSingapore program. Take me to the program of . Hier geht es zum Programm von Berlin Berlin .

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

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