Book Image

Codeless Deep Learning with KNIME

By : KNIME AG, Kathrin Melcher, Rosaria Silipo
Book Image

Codeless Deep Learning with KNIME

By: KNIME AG, Kathrin Melcher, 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

Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension

This section is introductory. It is here to ease you into the world of neural networks and the tool at hand, KNIME Analytics Platform.

This section comprises the following chapters:

  • Chapter 1, Introduction to Deep Learning with KNIME Analytics Platform
  • Chapter 2, Data Access and Preprocessing with KNIME Analytics Platform
  • Chapter 3, Getting Started with Neural Networks
  • Chapter 4, Building and Training a Feedforward Neural Network