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

Summary

We have reached the end of this relatively long chapter. Here, we have described three NLP case studies, each one solved by training an LSTM-based RNN applied to a time series prediction kind of problem.

The first case study analyzed movie review texts to extract the sentiment hidden in it. We dealt there with a simplified problem, considering a binary classification (positive versus negative) rather than considering too many nuances of possible user sentiment.

The second case study was language modeling. Training an RNN on a given text corpus with a given style produced a network capable of generating free text in that given style. Depending on the text corpus on which the network is trained, it can produce fairy tales, Shakespearean dialogue, or even rap songs. We showed an example that generates text in fairy tale style. The same workflows can be easily extended with more success to generate rap songs (R. Silipo, AI generated rap songs, CustomerThink, 2019, https:...

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Codeless Deep Learning with KNIME

Published in: Nov 2020Publisher: PacktISBN-13: 9781800566613

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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You're reading from Codeless Deep Learning with KNIME

Summary

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