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This masterclass will introduce the problem of data scarcity for ML models from various perspectives. We will then look at various Data Augmentation techniques for image and sound datasets along with the opportunities afforded by simulators for creating synthetic data. Finally, we will see some real examples of ML models whose accuracy has been significantly improved by Data Augmentation-based training.

Addressed to:

– Data science analysts and experts

Programa

– Introduction to the problem of data scarcity in ML
– Data Augmentation techniques
– Transformation techniques
– Combination techniques
– Data simulation
– Practical example

Masterclass taught by the i2Cat Foundation (CIDAI core partner)

Taught by:

Ivan Huerta Casado
Senior R&D Engineer in Artificial Intelligence at i2CAT

Josep Escrig, Director of the Distributed Artificial Intelligence research area at i2CAT