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

Introduction to CNNs

CNNs are commonly used in image processing and have been the winning models in several image-processing competitions. They are often used, for example, for image classification, object detection, and semantic segmentation.

Sometimes, CNNs are also used for non-image-related tasks, such as recommendation systems, videos, or time-series analysis. Indeed, CNNs are not only applied to two-dimensional data with a grid structure but can also work when applied to one- or three-dimensional data. In this chapter, however, we focus on the most common CNN application area: image processing.

A CNN is a neural network with at least one convolution layer. As the name states, convolution layers perform a convolution mathematical transformation on the input data. Through such a mathematical transformation, convolution layers acquire the ability to detect and extract a number of features from an image, such as edges, corners, and shapes. Combinations of such extracted features...