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Codeless Deep Learning with KNIME
Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME: Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

By Kathrin Melcher , KNIME AG , Rosaria Silipo
$54.99
Book Nov 2020 384 pages 1st Edition
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Codeless Deep Learning with KNIME

Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform

We'll start our journey of exploring Deep Learning (DL) paradigms by looking at KNIME Analytics Platform. If you have always been drawn to neural networks and deep learning architectures and have always thought that the coding part would be an obstacle to you developing a quick learning curve, then this is the book for you.

Deep learning can be quite complex, and we must make sure that the journey is worth the result. Thus, we'll start this chapter by stating, once again, the relevance of deep learning techniques when it comes to successfully implementing applications for data science.

We will continue by providing a quick overview of the tool of choice for this book – KNIME Software – and focus on how it complements both KNIME Analytics Platform and KNIME Server.

The work we'll be doing throughout this book will be implemented in KNIME Analytics Platform, which is open source...

The Importance of Deep Learning

If you have been working in the field of data science – or Artificial Intelligence (AI), as it is called nowadays – for a few years, you might have noticed the recent sudden explosion of scholarly and practitioner articles about successful solutions based on deep learning techniques.

The big breakthrough happened in 2012 when the deep learning-based AlexNet network won the ImageNet challenge by an unprecedented margin. This victory kicked off a surge in the usage of deep learning networks. Since then, these have expanded to many different domains and tasks.

So, what are we referring to exactly when we talk about deep learning? Deep learning covers a subset of Machine Learning (ML) algorithms, most of which stem from neural networks. Deep learning is indeed the modern evolution of traditional neural networks. Apart from the classic feedforward, fully connected, backpropagation-trained, and multilayer perceptron architectures, deeper...

Exploring KNIME Software

We will mainly be working with two KNIME products: KNIME Analytics Platform and KNIME Server. KNIME Analytics Platform includes ML and deep learning algorithms and data operations needed for data science projects. KNIME Server, on the other hand, provides the IT infrastructure for easy and secure deployment, as well as model monitoring over time.

We'll concentrate on KNIME Analytics Platform first and provide an overview of what it can accomplish.

KNIME Analytics Platform

KNIME Analytics Platform is an open source piece of software for all your data needs. It is free to download from the KNIME website (https://www.knime.com/downloads) and free to use. It covers all the main data wrangling and machine learning techniques available at the time of writing, and it is based on visual programming.

Visual programming is a key feature of KNIME Analytics Platform for quick prototyping. It makes the tool very easy to use. In visual programming, a Graphical...

Exploring KNIME Analytics Platform

To install KNIME Analytics Platform, follow these steps:

  1. Go to .
  2. Provide some details about yourself (step 1 in Figure 1.2).
  3. Download the version that's suitable for your operating system (step 2 in Figure 1.2).
  4. While you're waiting for the appropriate version to download, browse through the different steps to get started (step 3 in Figure 1.2):
Figure 1.2 – Steps for downloading the KNIME Analytics Platform package

Figure 1.2 – Steps for downloading the KNIME Analytics Platform package

Once you've downloaded the package, locate it, start it, and follow the instructions that appear onscreen to install it in any directory that you have write permissions for.

Once it's been installed, locate your instance of KNIME Analytics Platform – from the appropriate folder, desktop link, application, or link in the start menu – and start it.

When the splash screen appears, a window will ask for the location of your workspace (Figure...

Installing KNIME Deep Learning – Keras Integration

In this section, you will learn how to install and set up KNIME Deep Learning - Keras Integration in order to train neural networks in KNIME Analytics Platform.

KNIME Analytics Platform consists of a software core and several provided extensions and integrations. Such extensions and integrations are provided by the KNIME community and extend the original software core through a variety of data science functionalities, including advanced algorithms for AI.

The KNIME extension of interest here is called KNIME Deep Learning – Keras Integration. It offers a codeless GUI-based integration of the Keras library, while using TensorFlow as its backend. This means that a number of functions from Keras libraries have been wrapped into KNIME nodes, within KNIME's classic, easy-to-use visual dialog window. Due to this integration, you can read, write, create, train, and execute deep learning networks without writing code...

Goal and Structure of this Book

In this book, our aim is to provide you with a strong theoretical basis about deep learning architectures and training paradigms, as well as some detailed codeless experience of their implementations for solving practical case studies based on real-world data.

For this journey, we have adopted the codeless tool, KNIME Analytics Platform. KNIME Analytics Platform is based on visual programming and exploits a user-friendly GUI to make data analytics a more affordable task without the barrier of coding. As with many other external extensions, KNIME Analytics Platform has integrated the Keras libraries under this same GUI, thus including deep learning as part of its list of codeless extensions. From within KNIME Analytics Platform, you can build, train, and test a deep learning architecture with just a few drag and drops and a few clicks of the mouse. We provided a little introduction to the tool in this chapter, but we will provide more detailed information...

Summary

This first chapter aimed to prepare you for the content provided in this book.

Thus, we started this chapter by reminding you of the importance of deep learning, as well as the surge in popularity it garnered following the first deep learning success stories. Such a surge in popularity is probably what brought you here, with the desire to learn more about practical implementations of deep learning networks for real use cases.

Nowadays, the main barrier that we come across when learning about deep learning is the coding skills that are required. Here, we adopted KNIME software, and in particular the open source KNIME Analytics Platform, so that we can look at the case studies that will be proposed throughout this book. To do this, we described KNIME software and KNIME Analytics Platform in detail.

KNIME Analytics Platform also benefits from an extension known as KNIME Deep Learning – Keras Integration, which helps with integrating Keras deep learning libraries...

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Key benefits

  • Become well-versed with KNIME Analytics Platform to perform codeless deep learning
  • Design and build deep learning workflows quickly and more easily using the KNIME GUI
  • Discover different deployment options without using a single line of code with KNIME Analytics Platform

Description

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.

What you will learn

Use various common nodes to transform your data into the right structure suitable for training a neural network Understand neural network techniques such as loss functions, backpropagation, and hyperparameters Prepare and encode data appropriately to feed it into the network Build and train a classic feedforward network Develop and optimize an autoencoder network for outlier detection Implement deep learning networks such as CNNs, RNNs, and LSTM with the help of practical examples Deploy a trained deep learning network on real-world data
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Publication date : Nov 27, 2020
Length 384 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781800566613
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Publication date : Nov 27, 2020
Length 384 pages
Edition : 1st Edition
Language : English
ISBN-13 : 9781800566613
Category :
Concepts :

Table of Contents

16 Chapters
Preface Chevron down icon Chevron up icon
1. Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension Chevron down icon Chevron up icon
2. Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform Chevron down icon Chevron up icon
3. Chapter 2: Data Access and Preprocessing with KNIME Analytics Platform Chevron down icon Chevron up icon
4. Chapter 3: Getting Started with Neural Networks Chevron down icon Chevron up icon
5. Chapter 4: Building and Training a Feedforward Neural Network Chevron down icon Chevron up icon
6. Section 2: Deep Learning Networks Chevron down icon Chevron up icon
7. Chapter 5: Autoencoder for Fraud Detection Chevron down icon Chevron up icon
8. Chapter 6: Recurrent Neural Networks for Demand Prediction Chevron down icon Chevron up icon
9. Chapter 7: Implementing NLP Applications Chevron down icon Chevron up icon
10. Chapter 8: Neural Machine Translation Chevron down icon Chevron up icon
11. Chapter 9: Convolutional Neural Networks for Image Classification Chevron down icon Chevron up icon
12. Section 3: Deployment and Productionizing Chevron down icon Chevron up icon
13. Chapter 10: Deploying a Deep Learning Network Chevron down icon Chevron up icon
14. Chapter 11: Best Practices and Other Deployment Options Chevron down icon Chevron up icon
15. Other Books You May Enjoy Chevron down icon Chevron up icon

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