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You're reading from  Codeless Deep Learning with KNIME

Product typeBook
Published inNov 2020
Reading LevelIntermediate
PublisherPackt
ISBN-139781800566613
Edition1st Edition
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Authors (3):
Kathrin Melcher
Kathrin Melcher
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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

Rosaria Silipo
Rosaria Silipo
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Rosaria Silipo

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).
Read more about Rosaria Silipo

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Building and Training the Encoder-Decoder Architecture

Now that the three sequences are available, we can start defining the network structure within a workflow. In this section, you will learn how to define and train an encoder-decoder structure in KNIME Analytics Platform. Once the network is trained, you will learn how the encoder and decoder can be extracted into two networks. In the last section, we will discuss how the extracted networks can be used in a deployment workflow to translate English sentences into German.

Defining the Network Structure

In the encoder-decoder architecture, we want to have both the encoder and the decoder as LSTM networks. The encoder and the decoder have different input sequences. The English one-hot-encoded sentences are the input for the encoder and the German one-hot-encoded sentences are the input for the decoder. This means two input layers are needed: one for the encoder and one for the decoder.

The encoder network is made up of two...

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Codeless Deep Learning with KNIME
Published in: Nov 2020Publisher: PacktISBN-13: 9781800566613

Authors (3)

author image
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

author image
Rosaria Silipo

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).
Read more about Rosaria Silipo