You're reading from Codeless Deep Learning with KNIME Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

Product type Paperback

Published in Nov 2020

Publisher Packt

ISBN-13 9781800566613

Length 384 pages

Edition 1st Edition

Languages

Python

Tools

Knime

Concepts

Deep Learning

Authors (3):

Kathrin Melcher

KNIME AG

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 FREE CHAPTER

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

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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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You're reading from Codeless Deep Learning with KNIME Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

Table of Contents (16) Chapters

Classifying Images with CNNs

Authors (3)

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You're reading from Codeless Deep Learning with KNIME Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

Table of Contents (16) Chapters

Classifying Images with CNNs

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Authors (3)

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