Reader small image

You're reading from  Codeless Deep Learning with KNIME

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

Rosaria Silipo
Rosaria Silipo
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

View More author details
Right arrow

Questions and Exercises

  1. What is the kernel size in a convolutional layer?

    a) The area summarized by a statistical value

    b) The size of the matrix moving across an image

    c) The number of pixels to shift the matrix

    d) The size of the area used by a layer

  2. What is a pooling layer?

    a) A pooling layer is a commonly used layer in RNNs

    b) A pooling layer summarizes an area with a statistical value

    c) A pooling layer is a commonly used layer in feedforward networks

    d) A pooling layer can be used to upsample images

  3. When is transfer learning helpful?

    a) To transfer data to another system

    b) If no model is available

    c) If not enough labeled data is available

    d) To compare different models

lock icon
The rest of the page is locked
Previous PageNext Chapter
You have been reading a chapter from
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