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Deep learning for the Earth Sciences - A comprehensive approach to remote sensing, climate science and geosciences (Hardcover)
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Deep learning for the Earth Sciences - A comprehensive approach to remote sensing, climate science and geosciences (Hardcover)
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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
treatment of the concepts, skills, and practices necessary to
quickly become familiar with the application of deep learning
techniques to the Earth sciences. The book prepares readers to be
ready to use the technologies and principles described in their own
research. The distinguished editors have also included resources
that explain and provide new ideas and recommendations for new
research especially useful to those involved in advanced research
education or those seeking PhD thesis orientations. Readers will
also benefit from the inclusion of: An introduction to deep
learning for classification purposes, including advances in image
segmentation and encoding priors, anomaly detection and target
detection, and domain adaptation An exploration of learning
representations and unsupervised deep learning, including deep
learning image fusion, image retrieval, and matching and
co-registration Practical discussions of regression, fitting,
parameter retrieval, forecasting and interpolation An examination
of physics-aware deep learning models, including emulation of
complex codes and model parametrizations Perfect for PhD students
and researchers in the fields of geosciences, image processing,
remote sensing, electrical engineering and computer science, and
machine learning, Deep Learning for the Earth Sciences will also
earn a place in the libraries of machine learning and pattern
recognition researchers, engineers, and scientists.
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