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

Test how well you have understood the concepts in this chapter by answering the following questions:

  1. A feedforward neural network is an architecture where:

    a. Each neuron from the previous layer is connected to each neuron in the next layer.

    b. There are auto and backward connections.

    c. There is just one unit in the output layer.

    d. There are as many input units as there are output units.

  2. Why do we need hidden layers in a feedforward neural network?

    a. For more computational power

    b. To speed up calculations

    c. To implement more complex functions

    d. For symmetry

  3. The backpropagation algorithm updates the network weights proportionally to:

    a. The output errors backpropagated through the network

    b. The input values forward propagated through the network

    c. The batch size

    d. The deltas calculated at the output layer and backpropagated through the network

  4. Which loss function is commonly used for a multiclass classification problem?

    a. MAE

    b. RMSE

    c. Categorical...