Codeless Deep Learning with KNIME

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• Codeless Deep Learning with KNIME
• Why subscribe?
• Contributors
• About the authors
• About the reviewers
• Packt is searching for authors like you
• Preface
  • Who this book is for
  • What this book covers
  • To get the most out of this book
  • Download the example workflows
  • Download the color images
  • Conventions used
  • Get in touch
  • Reviews
• 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
    • Training and Testing the Autoencoder
    • Detecting Fraudulent Transactions
  • Optimizing the Autoencoder Strategy
    • Optimizing Threshold
    • Threshold is defined on a separate subset of data, called the optimization set. There are two options here:
  • Deploying the Fraud Detector
    • Reading Network, New Transactions, and Normalization Parameters
    • Applying the Fraud Detector
    • Taking Actions
  • Summary
    • Questions and Exercises
• Chapter 6: Recurrent Neural Networks for Demand Prediction
  • Introducing RNNs
    • Recurrent Neural Networks
    • Recurrent Neural Units
    • Long Short-Term Memory
  • The Demand Prediction Problem
    • Demand Prediction
    • Predicting Energy Demand
  • Data Preparation Creating the Past
    • Data Loading and Standardization
    • Data Cleaning and Partitioning
    • Creating the Input Tensors
  • Building, Training, and Deploying an LSTM-Based RNN
    • Building the LSTM-Based RNN
    • Training the LSTM-Based RNN
    • Testing the LSTM-Based RNN
    • Building a Deployment Loop
    • Deploying the LSTM-Based RNN
  • Summary
  • Questions and Exercises
• Chapter 7: Implementing NLP Applications
  • Exploring Text Encoding Techniques for Neural Networks
    • Index Encoding
    • One-Hot Vector Encoding
    • Embeddings for Word Encoding
  • Finding the Tone of Your Customers Voice Sentiment Analysis
    • Preprocessing Movie Reviews
    • Defining and Training the Network Architecture
    • Executing and Evaluating the Network on the Test Set
  • Generating Free Text with RNNs
    • The Dataset
    • Predicting Words or Characters?
    • Preprocessing and Encoding
    • Defining and Training the Network Architecture
    • Building a Deployment Workflow
    • The New Fairy Tale
  • Generating Product Names with RNNs
    • The Problem of Product Name Generation
    • Preprocessing and Encoding Mountain Names
  • Defining and Training the Network Architecture
    • Building a Deployment Workflow
  • Summary
  • Questions and Exercises
• Chapter 8: Neural Machine Translation
  • Idea of Neural Machine Translation
  • Encoder-Decoder Architecture
    • Applying the Encoder
    • Applying the Decoder during Training
    • Applying the Decoder during Deployment
  • Preparing the Data for the Two Languages
  • Building and Training the Encoder-Decoder Architecture
    • Defining the Network Structure
    • Training the Network
    • Extracting the Trained Encoder and Decoder
    • Applying the Trained Network for Neural Machine Translation
  • Summary
  • Questions and Exercises
• Chapter 9: Convolutional Neural Networks for Image Classification
  • Introduction to CNNs
    • How are Images Stored?
    • Why do we need CNNs?
    • How does a Convolution Layer work?
    • Introducing Padding
    • Introducing Stride and Dilation Rate
    • Introducing Pooling
  • Classifying Images with CNNs
    • Reading and Preprocessing Images
    • Designing the Network
    • Training and Applying the Network
    • Prediction Extraction and Model Evaluation
  • Introduction to transfer learning
    • Why use Transfer Learning?
    • Formal Definition of Transfer Learning
    • Applying Transfer Learning
  • Applying Transfer Learning for Cancer Type Prediction
    • Downloading the Dataset
    • Reading and Preprocessing the Images
    • Training the Network
  • Summary
  • Questions and Exercises
• Section 3: Deployment and Productionizing
• Chapter 10: Deploying a Deep Learning Network
  • Conversion of the Network Structure
    • Saving a Trained Network
    • Reading a Trained Network
    • Using TensorFlow 2
  • Building a Simple Deployment Workflow
    • Building a Deployment Workflow Manually, without Integrated Deployment
    • Building a Deployment Workflow Automatically with Integrated Deployment
  • Improving Scalability GPU Execution
  • Summary
  • Questions and Exercises
• Chapter 11: Best Practices and Other Deployment Options
  • Building a Web Application
    • Introduction to KNIME WebPortal
    • Creating a Workflow to Run on KNIME WebPortal
    • Creating Composite Views
    • Shared Components
    • Building a WebPortal Application for Cancer Cell Classification
  • Building a Web Service with the REST Interface
    • Building a REST Service Workflow
  • KNIME Tips and Tricks
    • Shuffling Data during Training
    • Using Batch Normalization
    • Keeping Your Workflow Clean and Structured
    • Using the GroupBy Node and Pivoting Node to Avoid Loops
    • Specifying the Execution Order
  • Summary
  • Questions and Exercises
• Other Books You May Enjoy
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