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

Introducing RNNs

Let's start with an overview of RNNs.

RNNs are a family of neural networks that cannot be constrained in the feedforward architecture.

Important note

RNNs are obtained by introducing auto or backward connections – that is, recurrent connections – into feedforward neural networks.

When introducing a recurrent connection, we introduce the concept of time. This allows RNNs to take context into account; that is, to remember inputs from the past by capturing the dynamic of the signal.

Introducing recurrent connections changes the nature of the neural network from static to dynamic and is therefore suitable for analyzing time series. Indeed, RNNs are often used to create solutions to problems involving time-ordered sequences, such as time series analysis, language modeling, free text generation, automatic machine translation, speech recognition, image captioning, and other similar problems investigating the time evolution of a given signal...