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, Ph.D., now head of data science evangelism at KNIME, has spent 25+ years in applied AI, predictive analytics, and machine learning at Siemens, Viseca, Nuance Communications, and private consulting. Sharing her practical experience in a broad range of industries and deployments, including IoT, customer intelligence, financial services, social media, and cybersecurity, Rosaria has authored 50+ technical publications, including her recent books Guide to Intelligent Data Science (Springer) and Codeless Deep Learning with KNIME (Packt).

• Table of ContentsIntroduction to Deep Learning with KNIME Analytics PlatformData Access and Preprocessing with KNIME Analytics PlatformGetting Started with Neural Networks Building and Training a Feedforward Neural NetworkAutoencoder for Fraud DetectionRecurrent Neural Networks for Demand PredictionImplementing NLP ApplicationsNeural Machine TranslationConvolutional Neural Networks for Image ClassificationDeploying a Deep Learning NetworkBest Practices and Other Deployment Options

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, Ph.D., now head of data science evangelism at KNIME, has spent 25+ years in applied AI, predictive analytics, and machine learning at Siemens, Viseca, Nuance Communications, and private consulting. Sharing her practical experience in a broad range of industries and deployments, including IoT, customer intelligence, financial services, social media, and cybersecurity, Rosaria has authored 50+ technical publications, including her recent books Guide to Intelligent Data Science (Springer) and Codeless Deep Learning with KNIME (Packt).

• Table of ContentsIntroduction to Deep Learning with KNIME Analytics PlatformData Access and Preprocessing with KNIME Analytics PlatformGetting Started with Neural Networks Building and Training a Feedforward Neural NetworkAutoencoder for Fraud DetectionRecurrent Neural Networks for Demand PredictionImplementing NLP ApplicationsNeural Machine TranslationConvolutional Neural Networks for Image ClassificationDeploying a Deep Learning NetworkBest Practices and Other Deployment Options

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, Ph.D., now head of data science evangelism at KNIME, has spent 25+ years in applied AI, predictive analytics, and machine learning at Siemens, Viseca, Nuance Communications, and private consulting. Sharing her practical experience in a broad range of industries and deployments, including IoT, customer intelligence, financial services, social media, and cybersecurity, Rosaria has authored 50+ technical publications, including her recent books Guide to Intelligent Data Science (Springer) and Codeless Deep Learning with KNIME (Packt).

• Table of ContentsIntroduction to Deep Learning with KNIME Analytics PlatformData Access and Preprocessing with KNIME Analytics PlatformGetting Started with Neural Networks Building and Training a Feedforward Neural NetworkAutoencoder for Fraud DetectionRecurrent Neural Networks for Demand PredictionImplementing NLP ApplicationsNeural Machine TranslationConvolutional Neural Networks for Image ClassificationDeploying a Deep Learning NetworkBest Practices and Other Deployment Options