Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutions
▶Book Description
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.
▶What You Will Learn
⦁Use various common nodes to transform your data into the right structure suitable for training a neural network
⦁Understand neural network techniques such as loss functions, backpropagation, and hyperparameters
⦁Prepare and encode data appropriately to feed it into the network</li><li>Build and train a classic feedforward network
⦁Develop and optimize an autoencoder network for outlier detection
⦁Implement deep learning networks such as CNNs, RNNs, and LSTM with the help of practical examples
⦁Deploy a trained deep learning network on real-world data
▶Key Features
⦁Become well-versed with KNIME Analytics Platform to perform codeless deep learning
⦁Design and build deep learning workflows quickly and more easily using the KNIME GUI
⦁Discover different deployment options without using a single line of code with KNIME Analytics Platform
▶Who This Book Is For
This book is for data analysts, data scientists, and deep learning developers who are not well-versed in Python but want to learn how to use KNIME GUI to build, train, test, and deploy neural networks with different architectures. The practical implementations shown in the book do not require coding or any knowledge of dedicated scripts, so you can easily implement your knowledge into practical applications. No prior experience of using KNIME is required to get started with this book.
▶What this book covers
⦁ Chapter 1, Introduction to Deep Learning with KNIME Analytics Platform, is a preparation chapter to get you familiar with the tool and the recent popularity of deep learning techniques.
⦁ Chapter 2, Data Access and Preprocessing with KNIME Analytics Platform, dives a bit deeper into the basic and advanced functionalities of KNIME Analytics Platform: from data access to workflow parameterization.
⦁ Chapter 3, Getting Started with Neural Networks, is the only theoretical chapter of the book. It paints an overview of the basic concepts around neural and deep learning networks and the algorithms used to train them.
⦁ Chapter 4, Building and Training a Feedforward Neural Network, is where we put into practice what we describe in Chapter 3, Getting Started with Neural Networks; we will build, train, and evaluate our first simple feedforward networks for classification tasks.
⦁ Chapter 5, Autoencoder for Fraud Detection, is where, with a neural autoencoder to solve the problem of fraud detection in credit card transactions, we start the series of case studies based on deep learning solutions.
⦁ Chapter 6, Recurrent Neural Networks for Demand Prediction, is where we introduce Long Short-Term Memory (LSTM) models in recurrent neural networks. Indeed, with their dynamic behavior, they are particularly effective in solving time series problems, such as a classic demand prediction problem.
⦁ Chapter 7, Implementing NLP Applications, covers how LSTM-based recurrent neural networks are often also used to implement solutions for natural language processing tasks. In this chapter, we cover a few case studies for free text generation, free name generation, and sentiment analysis.
⦁ Chapter 8, Neural Machine Translation, looks at an encoder-decoder architecture for automatic translations.
⦁ Chapter 9, Convolutional Neural Networks for Image Classification, covers a case study on image classification, which we could not miss. We classify histopathology images into cancer diagnoses using a convolutional neural network.
⦁ Chapter 10, Deploying a Deep Learning Network, starts describing the deployment phase. A simple example of the deployment workflow is explained in detail.
⦁ Chapter 11, Best Practices and Other Deployment Options, extends the previous chapter dedicated to deployment with more deployment options, such as web applications and REST services, and we conclude the book with a few tips and tricks.
