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

  1. Which network conversions are available in KNIME Analytics Platform?

    a) Keras to TensorFlow network conversion

    b) TensorFlow to Keras network conversion

    c) ONNX to Keras network conversion

    d) Keras to ONNX network conversion

  2. Which statements regarding Integrated Deployment are true (two statements are correct)?

    a) Integrated Deployment allows us to retrain a model during execution.

    b) The execution of the automatically generated workflow can be triggered by another workflow.

    c) The execution of the training workflow is triggered by the deployment workflow.

    d) Integrated Deployment closes the gap between training and deployment.