Deep Learning in Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning in Science write by Pierre Baldi. This book was released on 2021-07. Deep Learning in Science available in PDF, EPUB and Kindle. Rigorous treatment of the theory of deep learning from first principles, with applications to beautiful problems in the natural sciences.
Deep Learning For Physics Research
Deep Learning For Physics Research - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning For Physics Research write by Martin Erdmann. This book was released on 2021-06-25. Deep Learning For Physics Research available in PDF, EPUB and Kindle. A core principle of physics is knowledge gained from data. Thus, deep learning has instantly entered physics and may become a new paradigm in basic and applied research.This textbook addresses physics students and physicists who want to understand what deep learning actually means, and what is the potential for their own scientific projects. Being familiar with linear algebra and parameter optimization is sufficient to jump-start deep learning. Adopting a pragmatic approach, basic and advanced applications in physics research are described. Also offered are simple hands-on exercises for implementing deep networks for which python code and training data can be downloaded.
Deep Learning for the Earth Sciences
Deep Learning for the Earth Sciences - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning for the Earth Sciences write by Gustau Camps-Valls. This book was released on 2021-08-18. Deep Learning for the Earth Sciences available in PDF, EPUB and Kindle. 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.
Deep Learning for the Life Sciences
Deep Learning for the Life Sciences - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning for the Life Sciences write by Bharath Ramsundar. This book was released on 2019-04-10. Deep Learning for the Life Sciences available in PDF, EPUB and Kindle. Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
Deep Learning for Hydrometeorology and Environmental Science
Deep Learning for Hydrometeorology and Environmental Science - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Deep Learning for Hydrometeorology and Environmental Science write by Taesam Lee. This book was released on 2021-01-27. Deep Learning for Hydrometeorology and Environmental Science available in PDF, EPUB and Kindle. This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples with real datasets of hydrometeorology (e.g. streamflow and temperature) and environmental science (e.g. water quality). Deep learning is known as part of machine learning methodology based on the artificial neural network. Increasing data availability and computing power enhance applications of deep learning to hydrometeorological and environmental fields. However, books that specifically focus on applications to these fields are limited. Most of deep learning books demonstrate theoretical backgrounds and mathematics. However, examples with real data and step-by-step explanations to understand the algorithms in hydrometeorology and environmental science are very rare. This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN) as well as the conventional artificial neural network model.
