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

Neural Networks and Deep Learning – Basic Concepts

All you hear about at the moment is deep learning. Deep learning stems from the traditional discipline of neural networks, in the realm of machine learning.

The field of neural networks has gone through a number of stop-and-go phases. Since the early excitement for the first perceptron in the '60s and the subsequent lull when it became evident what the perceptron could not do; through the renewed enthusiasm for the backpropagation algorithm applied to multilayer feedforward neural networks and the subsequent lull when it became apparent that training recurrent networks required hardware capabilities that were not available at the time; right up to today's new deep learning paradigms, units, and architectures running on much more powerful, possibly GPU-equipped, hardware.

Let's start from the beginning and, in this section, go through the basic concepts behind neural networks and deep learning. While these...