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Codeless Deep Learning with KNIME

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What you'll learn

The book begins with an easy introduction to KNIME Analytics Platform, covering traditional feed-forward neural networks, and then shows you how to use a backpropagation algorithm with the help of practical examples. You’ll also learn how to build simple and more complex neural networks within KNIME Analytics Platform, without using a single line of code. You will start with a simple feed-forward network to solve a simple classification problem on a small dataset. Having covered the basic concepts, you’ll move on to prepare data accordingly; apply best practices to avoid overfitting; and 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 the concluding chapters, you’ll explore practical and creative solutions for solving real-world data problems.

By the end of the book, you’ll have learned how to build a number of different neural architectures and will be able to train, test, and deploy the network.

A free copy of chapter one is available here for download.

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Who the book is for

This book is for data analysts, data scientists, and deep learning developers who are not well versed with 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, allowing you to quickly translate theoretical concepts into practical applications. No previous knowledge of KNIME is required.