This is the code repository for Codeless Deep Learning with KNIME, published by Packt.
Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform
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.
This book covers the following exciting features:
- 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
replace(upperCase($Gender$), "M", "Male")
Following is what you need for this book: This book is for data analysts, data scientists, and deep learning developers who are not well-versed in Python but want to learn how to use KNIME GUI to build, train, test, and deploy neural networks with different architectures. The practical implementations shown in the book do not require coding or any knowledge of dedicated scripts, so you can easily implement your knowledge into practical applications. No prior experience of using KNIME is required to get started with this book.
With the following software and hardware list you can run all code files present in the book (Chapter 1-11).
| Chapter | Software required | OS required |
|---|---|---|
| 1 | KNIME Analytics Platform | Windows, Mac OS X, and Linux (Any) |
| 2 | KNIME Server (optional) | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Kathrin Melcher is a data scientist at KNIME. She holds a master's degree in mathematics from the University of Konstanz, Germany. She joined the evangelism team at KNIME in 2017 and has a strong interest in data science and machine learning algorithms. She enjoys teaching and sharing her data science knowledge with the community, for example, in the book From Excel to KNIME, as well as on various blog posts and at training courses, workshops, and conference presentations.
Rosaria Silipo has been working in data analytics since 1992. Currently, she is a principal data scientist at KNIME. In the past, she has held senior positions with Siemens, Viseca AG, and Nuance Communications, and worked as a consultant in a number of data science projects. She holds a Ph.D. in bioengineering from the Politecnico di Milano and a master’s degree in electrical engineering from the University of Florence (Italy). She is the author of more than 50 scientific publications, many scientific white papers, and a number of books for data science practitioners.
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