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 10: Deploying a Deep Learning Network

In the previous sections of this book, we covered the training of deep neural networks for many different use cases, starting with an autoencoder for fraud detection, through Long Short-Term Memory (LSTM) networks for energy consumption prediction and free text generation, all the way to cancer cell classification. But training the network is not the only part of a project. Once a deep learning network is trained, the next step is to deploy it.

During the exploration of some of the use cases, a second workflow has already been introduced, to deploy the network to work on real-world data. So, you have already seen some deployment examples. In this last section of the book, however, we focus on the many deployment options for machine learning models in general, and for trained deep learning networks in particular.

Usually, a second workflow is built and dedicated to deployment. This workflow reads the trained model and the new real...