Deep learning for retail warehouse operations
Recent advances in deep learning have enabled research and industry to master many challenges in computer vision and natural language processing that were out of reach until just a few years ago. Yet these challenges represent only the tip of the iceberg of what is possible. In this talk, I will demonstrate how we have used deep neural networks in to solve a special case of the travelling salesman problem and in turn steer operations at Zalando’s fashion warehouses. Further, I will present a few tips and tricks on getting GPU enabled neural networks running with minimal technical overhead.
Calvin Seward is a Resarch Scientist for Zalando's Research Lab, working mainly on unsupervised and semi-supervised computer vision problems including classification, localization and semantic segmentation. The main line of attack for these problems is deep neural networks written in the Tensorflow framework and trained on GPU clusters. At the same time, he is involved in applying lessons from GPU-driven HPC computing and cutting edge Machine Learning to other fields of the Zalando universe. Past projects have included sizing solutions for online retail, warehouse management and recommendation systems.