This textbook introduces the reader to Machine Learning (ML) applications in Earth Sciences. It describes the main Python tools devoted to ML, the typical workflow of ML applications in Earth Sciences, and proceeds with reporting how ML algorithms work.
This textbook presents multiple facets of design, development and deployment of deep learning networks for both students and industry practitioners.
This book, written by leading international researchers of evolutionary approaches to machine learning, explores various ways evolution can address machine learning problems and improve current methods of machine learning.
This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods.
This Element aims to develop strategies that can be extended over the entire EM spectra and beyond, impacting the society of the robot-human alliance.
Generative Adversarial Networks (GANs) in Practice is an all-inclusive resource that provides a solid foundation on GAN methodologies, their application to real-world projects, and their underlying mathematical and theoretical concepts.
This book covers different aspects of optimization autonomous underwater vehicles and their propulsion systems... Læs mere
This book provides a comprehensive overview of machine learning algorithms and examines their application in complex decision-making systems in a service-oriented framework.
This book describes the application of machine learning modelling approaches in atomic layer deposition and presents detailed information on modelling, optimization, and prediction of the behaviour and characteristics of ALD for improved process quality control.
This book comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image... Læs mere