Machine Learning for Planetary Science

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

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  • Author : Joern Helbert
  • Publisher : Elsevier
  • Pages : 232 pages
  • ISBN : 0128187220
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKMachine Learning for Planetary Science

Machine Learning for Planetary Science

Machine Learning for Planetary Science
  • Author : Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
  • Publisher : Elsevier
  • Release : 25 March 2022
GET THIS BOOKMachine Learning for Planetary Science

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice

Machine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather
  • Author : Enrico Camporeale,Simon Wing,Jay Johnson
  • Publisher : Elsevier
  • Release : 31 May 2018
GET THIS BOOKMachine Learning Techniques for Space Weather

Machine Learning Techniques for Space Weather provides a thorough and accessible presentation of machine learning techniques that can be employed by space weather professionals. Additionally, it presents an overview of real-world applications in space science to the machine learning community, offering a bridge between the fields. As this volume demonstrates, real advances in space weather can be gained using nontraditional approaches that take into account nonlinear and complex dynamics, including information theory, nonlinear auto-regression models, neural networks and clustering algorithms.

Deep Learning for the Earth Sciences

Deep Learning for the Earth Sciences
  • Author : Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
  • Publisher : John Wiley & Sons
  • Release : 18 August 2021
GET THIS BOOKDeep Learning for the Earth Sciences

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and

Advances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy
  • Author : Michael J. Way,Jeffrey D. Scargle,Kamal M. Ali,Ashok N. Srivastava
  • Publisher : CRC Press
  • Release : 29 March 2012
GET THIS BOOKAdvances in Machine Learning and Data Mining for Astronomy

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines

Planetary Remote Sensing and Mapping

Planetary Remote Sensing and Mapping
  • Author : Bo Wu,Kaichang Di,Jürgen Oberst,Irina Karachevtseva
  • Publisher : CRC Press
  • Release : 29 October 2018
GET THIS BOOKPlanetary Remote Sensing and Mapping

The early 21st century marks a new era in space exploration. The National Aeronautics and Space Administration (NASA) of the United States, The European Space Agency (ESA), as well as space agencies of Japan, China, India, and other countries have sent their probes to the Moon, Mars, and other planets in the solar system. Planetary Remote Sensing and Mapping introduces original research and new developments in the areas of planetary remote sensing, photogrammetry, mapping, GIS, and planetary science resulting from

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences
  • Author : Anonim
  • Publisher : Academic Press
  • Release : 25 September 2020
GET THIS BOOKMachine Learning and Artificial Intelligence in Geosciences

Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well

Machine Learning on Mars

Machine Learning on Mars
  • Author : Hannah Rae Kerner
  • Publisher : Unknown Publisher
  • Release : 22 May 2022
GET THIS BOOKMachine Learning on Mars

There are more than 20 active missions exploring planets and small bodies beyond Earth in our solar system today. Many more have completed their journeys or will soon begin. Each spacecraft has a suite of instruments and sensors that provide a treasure trove of data that scientists use to advance our understanding of the past, present, and future of the solar system and universe. As more missions come online and the volume of data increases, it becomes more difficult for scientists

The Atlas of AI

The Atlas of AI
  • Author : Kate Crawford
  • Publisher : Yale University Press
  • Release : 06 April 2021
GET THIS BOOKThe Atlas of AI

The hidden costs of artificial intelligence, from natural resources and labor to privacy and freedom What happens when artificial intelligence saturates political life and depletes the planet? How is AI shaping our understanding of ourselves and our societies? In this book Kate Crawford reveals how this planetary network is fueling a shift toward undemocratic governance and increased inequality. Drawing on more than a decade of research, award-winning science, and technology, Crawford reveals how AI is a technology of extraction: from

Machine Learning

Machine Learning
  • Author : Yagang Zhang
  • Publisher : BoD – Books on Demand
  • Release : 01 February 2010
GET THIS BOOKMachine Learning

Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning. The author also attempts to promote a new design of thinking

Machine Learning in Action

Machine Learning in Action
  • Author : Peter Harrington
  • Publisher : Simon and Schuster
  • Release : 03 April 2012
GET THIS BOOKMachine Learning in Action

Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor

Advances in Subsurface Data Analytics

Advances in Subsurface Data Analytics
  • Author : Shuvajit Bhattacharya,Haibin Di
  • Publisher : Elsevier
  • Release : 27 May 2022
GET THIS BOOKAdvances in Subsurface Data Analytics

Advances in Subsurface Data Analytics: Traditional and Physics-Based Approaches brings together the fundamentals of popular and emerging machine learning (ML) algorithms with their applications in subsurface analysis, including geology, geophysics, petrophysics, and reservoir engineering. The book is divided into four parts: traditional ML, deep learning, physics-based ML, and new directions, with an increasing level of diversity and complexity of topics. Each chapter focuses on one ML algorithm with a detailed workflow for a specific application in geosciences. Some chapters also

Large-Scale Machine Learning in the Earth Sciences

Large-Scale Machine Learning in the Earth Sciences
  • Author : Ashok N. Srivastava,Ramakrishna Nemani,Karsten Steinhaeuser
  • Publisher : CRC Press
  • Release : 01 August 2017
GET THIS BOOKLarge-Scale Machine Learning in the Earth Sciences

From the Foreword: "While large-scale machine learning and data mining have greatly impacted a range of commercial applications, their use in the field of Earth sciences is still in the early stages. This book, edited by Ashok Srivastava, Ramakrishna Nemani, and Karsten Steinhaeuser, serves as an outstanding resource for anyone interested in the opportunities and challenges for the machine learning community in analyzing these data sets to answer questions of urgent societal interest...I hope that this book will inspire

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation
  • Author : Petr Skoda,Fathalrahman Adam
  • Publisher : Elsevier
  • Release : 10 April 2020
GET THIS BOOKKnowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data