Deep learning in the wild

Invited paperDeep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice

Circular solid state reduction process of fine copper powder synthesis with life cycle assessment for photovoltaics application

This paper presents the process of synthesis of copper powders obtained by the pyrometallurgical method without the participation of the liquid phase. This method is based on the simultaneous decomposition and reduction of copper (II) carbonate. Hydrogen was used as a reducing agent. Due to the strongly exothermic thermal effect of the reduction reaction, a mixture of inert gas and hydrogen was used to better control the parameters. Studies have shown that the carbonate method enables the synthesis of copper powders with a narrow distribution and controlled size. The size is controlled by the grinding time of the copper (II) carbonate. An life cycle assessment and circularity study evaluate the sustainability of the new process, and focus is given to the energy efficiency