Book Image

Codeless Deep Learning with KNIME

By : Kathrin Melcher, KNIME AG, Rosaria Silipo
Book Image

Codeless Deep Learning with KNIME

By: Kathrin Melcher, KNIME AG, Rosaria Silipo

Overview of this book

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.
Table of Contents (16 chapters)
1
Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
6
Section 2: Deep Learning Networks
12
Section 3: Deployment and Productionizing

Improving Scalability – GPU Execution

For the case studies described in this book, we have used relatively small datasets and small networks. This allowed us to train the networks within hours using only CPU-based execution. However, training tasks that take minutes or hours on small datasets can easily take days or weeks on larger datasets; small network architectures can quickly increase in size and execution times can quickly become prohibitive. In general, when working with deep neural networks, the training phase is the most resource-intensive task.

GPUs have been designed to handle multiple computations simultaneously. This paradigm suits the intensive computations required to train a deep learning network. Hence, GPUs are an alternative option to train large deep learning networks efficiently and effectively on large datasets.

Some Keras libraries can exploit the computational power of NVIDIA®-compatible GPUs via the TensorFlow paradigms. As a consequence,...