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 4: Building and Training a Feedforward Neural Network

In Chapter 3, Getting Started with Neural Networks, you learned the basic theory behind neural networks and deep learning. This chapter sets that knowledge into practice. We will implement two very simple classification examples: a multiclass classification using the iris flower dataset, and a binary classification using the adult dataset, also known as the census income dataset.

These two datasets are quite small and the corresponding classification solutions are also quite simple. A fully connected feedforward network will be sufficient in both examples. However, we decided to show them here as toy examples to describe all of the required steps to build, train, and apply a fully connected feedforward classification network with KNIME Analytics Platform and KNIME Keras Integration.

These steps include commonly used preprocessing techniques, the design of the neural architecture, the setting of the activation functions...