Book Image

Codeless Deep Learning with KNIME

By : Kathrin Melcher, KNIME AG, Rosaria Silipo
Book Image

Codeless Deep Learning with KNIME

By: Kathrin Melcher, KNIME AG, Rosaria Silipo

Overview of this book

KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems. Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you’ll learn how to prepare data, encode incoming data, and apply best practices. By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network.
Table of Contents (16 chapters)
1
Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
6
Section 2: Deep Learning Networks
12
Section 3: Deployment and Productionizing

Summary

In this chapter, we explored CNNs, focusing on image data.

We started with an introduction to convolution layers, which motivates the name of this new family of neural networks. In this introduction, we explained why CNNs are so commonly used for image data, how convolutional networks work, and the impact of the many setting options. Next, we discussed pooling layers, commonly used in CNNs to efficiently downsample the data.

Finally, we put all this knowledge to work by building and training from scratch a CNN to classify images of digits between 0 and 9 from the MNIST dataset. Afterward, we discussed the concept of transfer learning, introduced four scenarios in which transfer learning can be applied, and showed how we can use transfer learning in the field of neural networks.

In the last section, we applied transfer learning to train a CNN to classify histopathology slide images. Instead of training it from scratch, this time we reused the convolutional layers of...