Codeless Deep Learning with KNIME: Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform.
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...
Gespeichert in:
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham :
Packt Publishing, Limited,
2020.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | Description based upon print version of record. |
| Beschreibung: | 1 online resource (385 p.) |
| ISBN: | 180056242X 9781800562424 |
Internformat
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| 008 | 201205s2020 xx o ||| 0 eng d | ||
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| 049 | |a MAIN | ||
| 100 | 1 | |a Melcher, Kathrin. | |
| 245 | 1 | 0 | |a Codeless Deep Learning with KNIME |h [electronic resource] : |b Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. |
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2020. | ||
| 300 | |a 1 online resource (385 p.) | ||
| 500 | |a Description based upon print version of record. | ||
| 520 | |a 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. | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 650 | 0 | |a Data mining. |0 http://id.loc.gov/authorities/subjects/sh97002073 | |
| 650 | 0 | |a Machine learning. |0 http://id.loc.gov/authorities/subjects/sh85079324 | |
| 650 | 0 | |a Natural language processing (Computer science) |0 http://id.loc.gov/authorities/subjects/sh88002425 | |
| 650 | 2 | |a Data Mining |0 https://id.nlm.nih.gov/mesh/D057225 | |
| 650 | 2 | |a Natural Language Processing |0 https://id.nlm.nih.gov/mesh/D009323 | |
| 650 | 2 | |a Machine Learning |0 https://id.nlm.nih.gov/mesh/D000069550 | |
| 650 | 6 | |a Exploration de données (Informatique) | |
| 650 | 6 | |a Apprentissage automatique. | |
| 650 | 6 | |a Traitement automatique des langues naturelles. | |
| 650 | 7 | |a Natural language & machine translation. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Pattern recognition. |2 bicssc | |
| 650 | 7 | |a Computer vision. |2 bicssc | |
| 650 | 7 | |a Computers |x Computer Vision & Pattern Recognition. |2 bisacsh | |
| 650 | 7 | |a Computers |x Natural Language Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Data mining |2 fast | |
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| 758 | |i has work: |a Codeless deep learning with KNIME (Text) |1 https://id.oclc.org/worldcat/entity/E39PCFHPgG8pJ46M4kpPKCvQ4m |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Melcher, Kathrin |t Codeless Deep Learning with KNIME : Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform |d Birmingham : Packt Publishing, Limited,c2020 |z 9781800566613 |
| 856 | 4 | 0 | |l FWS01 |p ZDB-4-EBA |q FWS_PDA_EBA |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2695317 |3 Volltext |
| 938 | |a Askews and Holts Library Services |b ASKH |n AH37976773 | ||
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| author | Melcher, Kathrin |
| author2 | Silipo, Rosaria |
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| author_facet | Melcher, Kathrin Silipo, Rosaria |
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| collection | ZDB-4-EBA |
| 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 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 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 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 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 |
| ctrlnum | (OCoLC)1225554944 |
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| dewey-search | 006.3/12 |
| dewey-sort | 16.3 212 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-on1225554944 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:30:08Z |
| institution | BVB |
| isbn | 180056242X 9781800562424 |
| language | English |
| oclc_num | 1225554944 |
| open_access_boolean | |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (385 p.) |
| psigel | ZDB-4-EBA |
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| publisher | Packt Publishing, Limited, |
| record_format | marc |
| spelling | Melcher, Kathrin. Codeless Deep Learning with KNIME [electronic resource] : Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. Birmingham : Packt Publishing, Limited, 2020. 1 online resource (385 p.) Description based upon print version of record. 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. 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 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 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 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 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 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Data Mining https://id.nlm.nih.gov/mesh/D057225 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Exploration de données (Informatique) Apprentissage automatique. Traitement automatique des langues naturelles. Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Pattern recognition. bicssc Computer vision. bicssc Computers Computer Vision & Pattern Recognition. bisacsh Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Data mining fast Machine learning fast Natural language processing (Computer science) fast Silipo, Rosaria. has work: Codeless deep learning with KNIME (Text) https://id.oclc.org/worldcat/entity/E39PCFHPgG8pJ46M4kpPKCvQ4m https://id.oclc.org/worldcat/ontology/hasWork 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 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2695317 Volltext |
| spellingShingle | Melcher, Kathrin Codeless Deep Learning with KNIME Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. 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 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 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 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 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 Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Data Mining https://id.nlm.nih.gov/mesh/D057225 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Exploration de données (Informatique) Apprentissage automatique. Traitement automatique des langues naturelles. Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Pattern recognition. bicssc Computer vision. bicssc Computers Computer Vision & Pattern Recognition. bisacsh Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Data mining fast Machine learning fast Natural language processing (Computer science) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh97002073 http://id.loc.gov/authorities/subjects/sh85079324 http://id.loc.gov/authorities/subjects/sh88002425 https://id.nlm.nih.gov/mesh/D057225 https://id.nlm.nih.gov/mesh/D009323 https://id.nlm.nih.gov/mesh/D000069550 |
| title | Codeless Deep Learning with KNIME Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. |
| title_auth | Codeless Deep Learning with KNIME Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. |
| title_exact_search | Codeless Deep Learning with KNIME Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. |
| title_full | Codeless Deep Learning with KNIME [electronic resource] : Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. |
| title_fullStr | Codeless Deep Learning with KNIME [electronic resource] : Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. |
| title_full_unstemmed | Codeless Deep Learning with KNIME [electronic resource] : Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. |
| title_short | Codeless Deep Learning with KNIME |
| title_sort | codeless deep learning with knime build train and deploy various deep neural network architectures using knime analytics platform |
| title_sub | Build, Train, and Deploy Various Deep Neural Network Architectures Using KNIME Analytics Platform. |
| topic | Data mining. http://id.loc.gov/authorities/subjects/sh97002073 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Natural language processing (Computer science) http://id.loc.gov/authorities/subjects/sh88002425 Data Mining https://id.nlm.nih.gov/mesh/D057225 Natural Language Processing https://id.nlm.nih.gov/mesh/D009323 Machine Learning https://id.nlm.nih.gov/mesh/D000069550 Exploration de données (Informatique) Apprentissage automatique. Traitement automatique des langues naturelles. Natural language & machine translation. bicssc Neural networks & fuzzy systems. bicssc Pattern recognition. bicssc Computer vision. bicssc Computers Computer Vision & Pattern Recognition. bisacsh Computers Natural Language Processing. bisacsh Computers Neural Networks. bisacsh Data mining fast Machine learning fast Natural language processing (Computer science) fast |
| topic_facet | Data mining. Machine learning. Natural language processing (Computer science) Data Mining Natural Language Processing Machine Learning Exploration de données (Informatique) Apprentissage automatique. Traitement automatique des langues naturelles. Natural language & machine translation. Neural networks & fuzzy systems. Pattern recognition. Computer vision. Computers Computer Vision & Pattern Recognition. Computers Natural Language Processing. Computers Neural Networks. Data mining Machine learning |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2695317 |
| work_keys_str_mv | AT melcherkathrin codelessdeeplearningwithknimebuildtrainanddeployvariousdeepneuralnetworkarchitecturesusingknimeanalyticsplatform AT siliporosaria codelessdeeplearningwithknimebuildtrainanddeployvariousdeepneuralnetworkarchitecturesusingknimeanalyticsplatform |