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Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME

By : KNIME AG , Melcher, Rosaria Silipo
4.5 (10)
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Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME

4.5 (10)
By: KNIME AG , 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)
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1
Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
6
Section 2: Deep Learning Networks
12
Section 3: Deployment and Productionizing

Chapter 3: Getting Started with Neural Networks

Before we dive into the practical implementation of deep learning networks using KNIME Analytics Platform and its integration with the Keras library, we will briefly introduce a few theoretical concepts behind neural networks and deep learning. This is the only purely theoretical chapter in this book, and it is needed to understand the how and why of the following practical implementations.

Throughout this chapter, we will cover the following topics:

  • Neural Networks and Deep Learning – Basic Concepts
  • Designing your Network
  • Training a Neural Network

We will start with the basic concepts of neural networks and deep learning: from the first artificial neuron as a simulation of the biological neuron to the training of a network of neurons, a fully connected feedforward neural network, using a backpropagation algorithm.

We will then discuss the design of a neural architecture as well as the training of the...

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Codeless Deep Learning with KNIME
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