Kathrin Melcher - Codeless Deep Learning with KNIME

Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

Kathrin Melcher

Rosaria Silipo

BIRMINGHAMMUMBAI

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Kathrin Melcher is a data scientist at KNIME. She holds a master's degree in mathematics from the University of Konstanz, Germany. She joined the evangelism team at KNIME in 2017 and has a strong interest in data science and machine learning algorithms. She enjoys teaching and sharing her data science knowledge with the community, for example, in the book From Excel to KNIME, as well as on various blog posts and at training courses, workshops, and conference presentations.

Rosaria Silipo has been working in data analytics since 1992. Currently, she is a principal data scientist at KNIME. In the past, she has held senior positions with Siemens, Viseca AG, and Nuance Communications, and worked as a consultant in a number of data science projects. She holds a Ph.D. in bioengineering from the Politecnico di Milano and a master's degree in electrical engineering from the University of Florence (Italy). She is the author of more than 50 scientific publications, many scientific white papers, and a number of books for data science practitioners.

There are so many people to thank! We would like to thank Corey Weisinger for the Demand Prediction workflow in Chapter 6, and Jon Fuller for the image classification workflow in Chapter 9; Marcel Wiedenmann, Christian Dietz, and Benjamin Wilhelm, from the KNIME development team, for the great Keras integration and the many deep learning nodes; and finally, Paolo Tamagnini and Maarit Widmann, from the components team at KNIME, for the shared components we used in this book.

Corey Weisinger is a data scientist at KNIME in Austin, Texas. He studied mathematics at Michigan State University, focusing on actuarial techniques and functional analysis. Prior to KNIME, he worked as an analytics consultant for the auto industry in Detroit, Michigan. He currently focuses on signal processing and numeric prediction techniques, teaches a time series course on KNIME, and is the author of the guidebook, From Alteryx to KNIME.

Adrian Nembach has a master's degree in computer science from the University of Konstanz. During his master's, he focused on deep learning for computer vision, including generative adversarial networks for semi-supervised classification of cell images and depth extraction from light field images. Alongside his studies, he also worked as a working student at KNIME, where he was involved in the development of various machine learning-related nodes and extensions, including integrations for Keras, XGBoost, and a rewrite of KNIME's native logistic regression and random forest nodes. After completing his degree, he started as a software engineer at KNIME, developing nodes for machine learning interpretability, active learning, and weak supervision.

Barbora Stetinova is experienced in the data science and business intelligence spheres. She started her career, after obtaining her MA and MBA degrees from university, at WITTE Automotive. Her data journey began in the controlling department as a data analyst, and she currently works in the IT department, where she is responsible for data science and business intelligence projects. Parallel to this, Barbora is engaged as a business analyst consultant for different industries at Leadership Synergy Community. To help others on their data science journey, she publishes her own data science e-learning courses. All of this led her to cooperate with Packt on data science projects as an e-learning trainer and technical reviewer, and with KNIME AG on a data visualization course.

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