The Chinese literati on painting : Su Shih (1037-1101) to Tung Ch'i-Ch'ang (1555-1636) / / Susan Bush

• Record Nr.: UNINA9910954353303321
• Autore: Bush Susan
• Pubbl/distr/stampa: Hong Kong, : Hong Kong University Press, 2012
• ISBN: 988-220-874-6
• Edizione: [[2nd ed.?].]
• Descrizione fisica: 1 online resource (244 p.)
• Disciplina: 759.951
• Soggetti: Painting, Chinese
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: First edition published by the Harvard-Yenching Institute, 1971.
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Contents; Preface to the Second Edition; Preface to the First Edition; Illustrations; Abbreviations; 1. Northern Sung (960-1127); 2. The Views of Northern Sung Literati; 3. Chin (1122-1234) and Southern Sung (1127-1260); 4. Yüan (1260-1368); 5. Ming (1368-1644); 6. Conclusion; Chinese Texts; Bibliography; Glossary; Index
• Sommario/riassunto: This classic work, first published in 1971, explores the transition in painting styles from the late Sung period to the art of Yuan dynasty literati. Building on the pioneering work of Oswald Siren and James Cahill, Susan Bush's investigations of painting done under the Chin dynasty confirmed the dominance of scholar-artists in the north and their gradual development of scholarly painting traditions, and a related study of Northern Sung writings showed that their theory was shaped as much by the views of their social class as by their artistic aims. Bush's perspective on Sung scholars' art and

Fundamentals of Machine Learning and Deep Learning in Medicine / / by Reza Borhani, Soheila Borhani, Aggelos K. Katsaggelos

• Record Nr.: UNINA9910631085603321
• Autore: Borhani Reza
• Pubbl/distr/stampa: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• ISBN: 3-031-19502-7
• Edizione: [1st ed. 2022.]
• Descrizione fisica: 1 online resource (201 pages)
• Collana: Medicine Series
• Disciplina: 006.31; 610.285631
• Soggetti: Internal medicine; Machine learning; Internal Medicine; Machine Learning; Aprenentatge automàtic; Intel·ligència artificial; Ús terapèutic; Llibres electrònics
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Introduction -- Mathematical Modeling of Medical Data -- Linear Learning -- Nonlinear Learning -- Multi-Layer Perceptrons -- Convolutional Neural Networks -- Recurrent Neural Networks -- Autoencoders -- Generative Adversarial Networks -- Reinforcement Learning.
• Sommario/riassunto: This book provides an accessible introduction to the foundations of machine learning and deep learning in medicine for medical students, researchers, and professionals who are not necessarily initiated in advanced mathematics but yearn for a better understanding of this disruptive technology and its impact on medicine. Once an esoteric subject known to few outside of computer science and engineering departments, today artificial intelligence (AI) is a widely popular technology used by scholars from all across the academic universe. In particular, recent years have seen a great deal of interest in the AI ; subfields of machine learning and deep learning from researchers in medicine and life sciences, evidenced by the rapid growth in the number of articles published on the topic in peer-reviewed medical journals over the last decade. The demand for high-quality educational resources in this area has never been greater than it is today, and will only continue to grow at a rapid pace. Expert authors remove the veil of unnecessary complexity that often surrounds machine learning and deep learning by employing a narrative style that emphasizes intuition in place of abstract mathematical formalisms, allowing them to strike a delicate balance between practicality and theoretical rigor in service of facilitating the reader’s learning experience. Topics covered in the book include: mathematical encoding of medical data, linear regression and classification, nonlinear feature engineering, deep learning, convolutional and recurrent neural networks, and reinforcement learning. Each chapter ends with a collection of exercises for readers to practice and test their knowledge. This is an ideal introduction for medical students, professionals, and researchers interested in learning more about machine learning and deep learning. Readers who have taken at least one introductory mathematics course at the undergraduate-level (e.g., biostatistics or calculus) will be well-equipped to use this book without needing any additional prerequisites. .