Codeless Deep Learning with KNIME
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Free ChapterSection 1: Feedforward Neural Networks and KNIME Deep Learning Extension
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Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform
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Chapter 2: Data Access and Preprocessing with KNIME Analytics Platform
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Chapter 3: Getting Started with Neural Networks
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Chapter 4: Building and Training a Feedforward Neural Network
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Section 2: Deep Learning Networks
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Chapter 5: Autoencoder for Fraud Detection
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Chapter 6: Recurrent Neural Networks for Demand Prediction
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Chapter 7: Implementing NLP Applications
- Chapter 7: Implementing NLP Applications
- Exploring Text Encoding Techniques for Neural Networks
- Finding the Tone of Your Customers' Voice – Sentiment Analysis
- Generating Free Text with RNNs
- Generating Product Names with RNNs
- Defining and Training the Network Architecture
- Summary
- Questions and Exercises
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Chapter 8: Neural Machine Translation
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Chapter 9: Convolutional Neural Networks for Image Classification
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Section 3: Deployment and Productionizing
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Chapter 10: Deploying a Deep Learning Network
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Chapter 11: Best Practices and Other Deployment Options
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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.
- Publication date:
- November 2020
- Publisher
- Packt
- Pages
- 384
- ISBN
- 9781800566613