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Codeless Deep Learning with KNIME

You're reading from  Codeless Deep Learning with KNIME

Product type Book
Published in Nov 2020
Publisher Packt
ISBN-13 9781800566613
Pages 384 pages
Edition 1st Edition
Languages
Concepts
Authors (3):
Kathrin Melcher Kathrin Melcher
Profile icon Kathrin Melcher
LinkedIn
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.
See other products by Kathrin Melcher
KNIME AG KNIME AG
Profile icon KNIME AG
LinkedIn
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Rosaria Silipo Rosaria Silipo
Profile icon Rosaria Silipo
LinkedIn
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).
See other products by Rosaria Silipo
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Table of Contents (16) Chapters

Preface 1. Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
2. Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform 3. Chapter 2: Data Access and Preprocessing with KNIME Analytics Platform 4. Chapter 3: Getting Started with Neural Networks 5. Chapter 4: Building and Training a Feedforward Neural Network 6. Section 2: Deep Learning Networks
7. Chapter 5: Autoencoder for Fraud Detection 8. Chapter 6: Recurrent Neural Networks for Demand Prediction 9. Chapter 7: Implementing NLP Applications 10. Chapter 8: Neural Machine Translation 11. Chapter 9: Convolutional Neural Networks for Image Classification 12. Section 3: Deployment and Productionizing
13. Chapter 10: Deploying a Deep Learning Network 14. Chapter 11: Best Practices and Other Deployment Options 15. Other Books You May Enjoy

Classifying Images with CNNs

In this section, we will see how to build and train from scratch a CNN for image classification.

The goal is to classify handwritten digits between 0 and 9 with the data from the MNIST database, a large database of handwritten digits commonly used for training various image-processing applications. The MNIST database contains 60,000 training images and 10,000 testing images of handwritten digits and can be downloaded from this website: http://yann.lecun.com/exdb/mnist/.

To read and preprocess images, KNIME Analytics Platform offers a set of dedicated nodes and components, available after installing the KNIME Image Processing Extension.

Tip

The KNIME Image Processing Extension (https://www.knime.com/community/image-processing) allows you to read in more than 140 different format types of images (thanks to the Bio-Formats Application Processing Interface (API)). In addition, it can be used to apply well-known image-processing techniques such as...

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Authors (3)

Left arrow icon
Profile icon Kathrin Melcher
LinkedIn
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.
See other products by Kathrin Melcher
Profile icon KNIME AG
LinkedIn
See other products by KNIME AG
Profile icon Rosaria Silipo
LinkedIn
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
See other products by Rosaria Silipo
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