Autoencoders are a neural network architecture that allows a network to learn from data without requiring a label for each data point. This session from the Machine Learning Conference explains the basic concept of autoencoders. We’ll go over several variants for autoencoders and different use cases. Join Christoph Henkelmann and find out more.
Christoph Henkelmann holds a degree in Computer Science from the University of Bonn. He is currently working at DIVISIO, an AI company from Cologne, where he is CTO and co-founder. At DIVISIO, he combines practical knowledge from two decades of server and mobile development with proven AI and ML technology. In his pastime he grows cacti, practices the piano and plays video games.
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