AutoML: time to evolve

Building deep learning models has been portrayed as playing with lego blocks. The success of the model highly depends on the configuration of its blocks. Hand designing new architectures for every variation of a problem can be time consuming. In this talk I will talk about using evolution to explore the structure space of a neural network.

Stathis holds a PhD in Robotics from Edinburgh University. Currently he leads the machine learning at AimBrain, where he works on deep learning models for mobile biometric authentication. Before joining AimBrain, he was a research engineer at Toshiba Medical Visualization Systems.

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