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  • Book Overview & Buying Codeless Deep Learning with KNIME
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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

Summary

We have reached the end of this chapter, where you have learned how to perform the different steps involved in training a neural network in KNIME Analytics Platform.

We started with common preprocessing steps, including different encodings, normalization, and missing value handling. Next, you learned how to define a neural network architecture by using different Keras layer nodes without writing code. We then moved on to the training of the neural network and you learned how to define the loss function, as well as how you can monitor the learning progress, apply the network to new data, and extract the predictions.

Each section closed with small example sessions, preparing you to perform all these steps on your own.

In the next chapter, you will see how these steps can be applied to the first use case of the book: fraud detection using an autoencoder.

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Codeless Deep Learning with KNIME
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