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

Chapter 9: Convolutional Neural Networks for Image Classification

In the previous chapters, we talked about Recurrent Neural Networks (RNNs) and how they can be applied to different types of sequential data and use cases. In this chapter, we want to talk about another family of neural networks, called Convolutional Neural Networks (CNNs). CNNs are especially powerful when used on data with grid-like topology and spatial dependencies, such as images or videos.

We will start with a general introduction to CNNs, explaining the basic idea behind a convolution layer and introducing some related terminology such as padding, pooling, filters, and stride.

Afterward, we will build and train a CNN for image classification from scratch. We will cover all required steps: from reading and preprocessing of the images to defining, training, and applying the CNN.

To train a neural network from scratch, a huge amount of labeled data is usually required. For some specific domains, such as...