Codeless deep learning with KNIME : build, train, and deploy various deep neural network architectures using KNIME analytics platform / Kathrin Melcher, Rosaria Silipo.

Format:

Book

Author/Creator:

Melcher, Kathrin, author.

Silipo, Rosaria, author.

Language:

English

Subjects (All):

Mineria de dades.

Physical Description:

1 online resource (x, 367 pages) : illustrations

Edition:

1st ed.

Place of Publication:

Birmingham, England ; Mumbai : Packt, [2020]

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

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

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

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

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

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

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

Building a Deployment Workflow

The New Fairy Tale

Generating Product Names with RNNs

The Problem of Product Name Generation

Preprocessing and Encoding Mountain Names

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

Extracting the Trained Encoder and Decoder

Applying the Trained Network for Neural Machine Translation

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

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

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

Other Books You May Enjoy

Index.

Notes:

Description based on print version record.

Description based on publisher supplied metadata and other sources.

ISBN:

9781800562424

180056242X

OCLC:

1225554944