| Description |
1 online resource (385 p.) |
| Note |
Description based upon print version of record. |
| Summary |
Starting with an easy introduction to KNIME Analytics Platform, this book will take you through the key features of the platform and cover the advanced and latest deep learning concepts in neural networks. In each chapter, you'll solve real-world case studies based on deep learning networks to spark your creativity for new projects. |
| Contents |
Cover -- Copyright -- About PACKT -- Contributors -- Table of Contents -- Preface -- Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension -- Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform -- The Importance of Deep Learning -- Exploring KNIME Software -- KNIME Analytics Platform -- KNIME Server for the Enterprise -- Exploring KNIME Analytics Platform -- Useful Links and Materials -- Build and Execute Your First Workflow -- Installing KNIME Deep Learning -- Keras Integration -- Installing the Keras and TensorFlow Nodes |
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Setting up the Python Environment -- Goal and Structure of this Book -- Summary -- Chapter 2: Data Access and Preprocessing with KNIME Analytics Platform -- Accessing Data -- Reading Data from Files -- Data Types and Conversions -- Transforming Data -- Parameterizing the Workflow -- Summary -- Questions and Exercises -- Chapter 3: Getting Started with Neural Networks -- Neural Networks and Deep Learning -- Basic Concepts -- Artificial Neuron and Artificial Neural Networks -- Signal Propagation within a Feedforward Neural Network -- Understanding the Need for Hidden Layers |
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Training a Multilayer Perceptron -- Designing your Network -- Commonly Used Activation Functions -- Regularization Techniques to Avoid Overfitting -- Other Commonly used Layers -- Training a Neural Network -- Loss Functions -- Parameters and Optimization of the Training Algorithm -- Summary -- Questions and Exercises -- Chapter 4: Building and Training a Feedforward Neural Network -- Preparing the Data -- Datasets and Classification Examples -- Encoding of Nominal Features -- Normalization -- Other Helpful Preprocessing Nodes -- Data Preparation on the Iris Dataset |
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Data Preparation on the Adult Dataset -- Building a Feedforward Neural Architecture -- The Keras Input Layer Node -- The Keras Dense Layer Node -- Building a Neural Network for Iris Flower Classification -- Building a Neural Network for Income Prediction -- Training the Network -- Selecting the Loss Function -- Defining the Input and Output Data -- Setting the Training Parameters -- Tracking the Training Progress -- Training Settings for Iris Flower Classification -- Training Settings for Income Prediction -- Testing and Applying the Network -- Executing the Network |
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Extracting the Predictions and Evaluating the Network Performance -- Testing the Network Trained to Classify Iris Flowers -- Testing the Network Trained for Income Prediction -- Summary -- Questions and Exercises -- Section 2: Deep Learning Networks -- Chapter 5: Autoencoder for Fraud Detection -- Introducing Autoencoders -- Architecture of the Autoencoder -- Reducing the Input Dimensionality with an Autoencoder -- Detecting Anomalies Using an Autoencoder -- Why is Detecting Fraud so Hard? -- Building and Training the Autoencoder -- Data Access and Data Preparation -- Building the Autoencoder |
| Local Note |
eBooks on EBSCOhost EBSCO eBook Subscription Academic Collection - North America |
| Subject |
Data mining.
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Machine learning.
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Natural language processing (Computer science)
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Natural language & machine translation. |
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Neural networks & fuzzy systems. |
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Pattern recognition. |
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Computer vision. |
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Computers -- Computer Vision & Pattern Recognition. |
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Computers -- Natural Language Processing. |
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Computers -- Neural Networks. |
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Data mining |
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Machine learning |
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Natural language processing (Computer science) |
| Added Author |
Silipo, Rosaria.
|
| Other Form: |
Print version: Melcher, Kathrin Codeless Deep Learning with KNIME : Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform Birmingham : Packt Publishing, Limited,c2020 9781800566613 |
| ISBN |
180056242X |
|
9781800562424 (electronic bk.) |
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