Daniel Cattarius
About Daniel Cattarius
Daniel Cattarius is a Senior Projektmanager at Finanz Informatik Solutions Plus GmbH, where he has worked since 2019. He has a background in applied computer science and extensive experience in neural network development and optimization.
Work at Finanz Informatik Solutions Plus
Daniel Cattarius has been employed at Finanz Informatik Solutions Plus GmbH since 2019, currently holding the position of Senior Projektmanager. His tenure at the company includes previous roles as Projektmanager from 2015 to 2018, and as Software-Ingenieur from 2007 to 2010. He also served as Senior Software-Ingenieur and Projektleitung from 2011 to 2014. Throughout his time at the company, he has worked in Frankfurt am Main.
Education and Expertise
Daniel Cattarius studied Angewandte Informatik at the University of Applied Sciences Kaiserslautern, where he achieved a Diplom-Informatiker (FH) from 2003 to 2007. Prior to this, he completed his studies in Fachinformatiker at IHK Rheinland-Pfalz, earning a Fachinformatiker (IHK) from 2000 to 2003. He also attended Technische Universität Kaiserslautern, focusing on Mathematik and Physik from 1998 to 2000.
Background
Daniel Cattarius has a strong background in software engineering and project management. His career at Finanz Informatik Solutions Plus GmbH spans over a decade, with experience in various roles that emphasize his expertise in software development and project leadership. His educational background in applied computer science and technical fields supports his professional endeavors.
Achievements in Neural Networks
Daniel Cattarius has made significant contributions to the field of neural networks. He has been involved in training neural networks using back propagation and developing algorithms for digit extraction from forms. His work includes optimizing neural network structures for improved recognition accuracy and co-authoring an article on handwriting recognition. He has also contributed to reducing handwriting recognition error rates to 2% and achieving a 98% accuracy rate in digit recognition.