You're reading from Codeless Deep Learning with KNIME

Product typeBook

Published inNov 2020

Reading LevelIntermediate

PublisherPackt

ISBN-139781800566613

Edition1st Edition

Languages

Python

Tools

Knime

Concepts

Deep Learning

Authors (3):

Kathrin Melcher

KNIME AG

Rosaria Silipo

View More author details

Section 2: Deep Learning Networks

Here, we move on to more advanced concepts in neural networks (deep learning) and how to implement them within KNIME Analytics Platform, based on some case studies.

This section comprises the following chapters:

Chapter 5, Autoencoder for Fraud Detection
Chapter 6, Recurrent Neural Networks for Demand Prediction
Chapter 7, Implementing NLP Applications
Anchor 8, Neural Machine Translation
Chapter 9, Convolutional Neural Networks for Image Classification

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Authors (3)

Kathrin Melcher

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.
Read more about Kathrin Melcher

KNIME AG

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