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

Questions and Exercises

Check your level of understanding of the concepts presented in this chapter by answering the following questions:

  1. How can you set the loss function to train your neural network?

    a) By using the Keras Loss Function node

    b) By using the Keras Output Layer node

    c) In the configuration window of the Keras Network Learner node

    d) In the configuration window of the Keras Network Executor node

  2. How can you one-hot encode your features?

    a) By using the One Hot Encoding node

    b) By using the One to Many node 

    c) By creating an integer encoding using the Category to Number node and afterward, the Integer to One Hot Encoding node

    d) By creating an integer encoding, transforming it into a collection cell, and selecting the right conversion

  3. How can you define the number of neurons for the input of your network?

    a) By using a Keras Input Layer node.

    b) By using a Keras Dense Layer node without any input network.

    c) The input dimension is set automatically based on...