Jinjin (29 Ergebnisse)

Zustand: New. In English.

Zustand: New. In English.

Taschenbuch. Zustand: Neu. Neuware - It's Tomorrow Somewhere is the debut collection of poems by Asian American poet e.jinjin. It's Tomorrow Somewhere presents readers with poetry exploring mental health and the effects it has on relationships with yourself and loved ones. It is a celebration of struggle and triumph on the road to self-discovery and self-love.…

Zustand: New. In English.

Zustand: New.

Zustand: New. In English.

Paperback. Zustand: Brand New. 105 pages. 9.25x6.10x9.25 inches. In Stock.

Paperback. Zustand: Brand New. 188 pages. 9.18x6.12x9.21 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Studienarbeit aus dem Jahr 2019 im Fachbereich Didaktik für das Fach Deutsch - Deutsch als Fremdsprache, DaF, Note: 1,7, Friedrich-Schiller-Universität Jena, Sprache: Deutsch, Abstract: In der Arbeit werden zunächst die Grundbegriffen Text und Textsorte definiert. Anschließend wird das analysierte Lehrwerk 'DaF kompakt B1' vorgestellt. Der Schwerpunkt wird auf die Textsorten und die dazugehörigen Lernziele, die Übungen nach dem vierstufigen Verfahren der Übungstypologie, die Authentizität nach den Kriterien von Edelhoff und die steile, lineare und grammatische Progression gelegt.Text ist eine der fundamentalen Größen sprachlicher Kommunikation. Der Sprachunterricht wird aus linguistischer Sicht als ein inter-textuelles Phänomen interpretiert, das sich in verschiedenen Ausprägungen darstellt. Der Lehr- und Lernprozess hat im engeren Sinne einen großen Zusammenhang mit dem Input von Texten und Textprodukten. Deshalb soll die Arbeit mit Texten eine zentrale Rolle beim Fremdsprachenerwerb spielen. In einem Lehrwerk gelten die Texte als das elementare und anschauliche Material, welche Informationen vom Zielland liefern, sprachliche Mittel wie Wortschatz und Grammatik präsentieren und Fertigkeiten entwickeln. Mithilfe von Texten kann man sich unter anderem auch eigene Gedanken machen, kommunikative Kompetenzen trainieren, ästhetisch lernen und die eigene Persönlichkeit sowie Charakter bilden.…

Paperback. Zustand: Brand New. 323 pages. 7.01x0.81x10.00 inches. In Stock.

HRD. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. Yunong Zhang, PH.D., earned his B.S. degree from Huazhong University of Science and Technology, Wuhan, China, in 1996, his M.S. degree from South China University of Technology, Guangzhou, China, in 1999, and his Ph.D. from the Chinese University .

Taschenbuch. Zustand: Neu. Neuware - This book aims to solve the discrete implementation problems of continuous-time neural network models while improving the performance of neural networks by using various Zhang Time Discretization (ZTD) formulas.The authors summarize and present the systematic derivations and complete research of ZTD formulas from special 3S-ZTD formulas to general NS-ZTD formulas. These finally lead to their proposed discrete-time Zhang neural network (DTZNN) algorithms, which are more efficient, accurate, and elegant. This book will open the door to scientific and engineering applications of ZTD formulas and neural networks, and will be a major inspiration for studies in neural network modeling, numerical algorithm design, prediction, and robot manipulator control.The book will benefit engineers, senior undergraduates, graduate students, and researchers in the fields of neural networks, computer mathematics, computer science, artificial intelligence, numerical algorithms, optimization, robotics, and simulation modeling.…

Zustand: New.

Zustand: New.

Zustand: New.

Zustand: New.

Buch. Zustand: Neu. Machine Learning in Protein Science | Efficient Prediction of Protein Structures and Properties | Jinjin Li (u. a.) | Buch | 240 S. | Englisch | 2026 | Wiley-VCH GmbH | EAN 9783527352159 | Verantwortliche Person für die EU: Wiley-VCH GmbH, Boschstr. 12, 69469 Weinheim, product-safety[at]wiley[dot]com | Anbieter: preigu. …

Buch. Zustand: Neu. Neuware -Harness the power of machine learning for quick and efficient calculations of protein structures and properties Machine Learning in Protein Science is a unique and practical reference that shows how to employ machine learning approaches for full quantum mechanical (FQM) calculations of protein structures and properties, thereby saving costly computing time and making this technology available for routine users. Machine Learning in Protein Science provides comprehensive coverage of topics including: - Machine learning models and algorithms, from deep neural network (DNN) and transfer learning (TL) to hybrid unsupervised and supervised learning - Protein structure predictions with AlphaFold to predict the effects of point mutations - Modeling and optimization of the catalytic activity of enzymes - Property calculations (energy, force field, stability, protein-protein interaction, thermostability, molecular dynamics) - Protein design and large language models (LLMs) of protein systems Machine Learning in Protein Science is an essential reference on the subject for biochemists, molecular biologists, theoretical chemists, biotechnologists, and medicinal chemists, as well as students in related programs of study.Wiley-VCH GmbH, Boschstraße 12, 69469 Weinheim 240 pp. Englisch. …

Zustand: New. In English.

Hardcover. Zustand: Brand New. 272 pages. 6.69x0.59x9.61 inches. In Stock.

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation.The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses.…

Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces readers to the fundamentals of and recent advances in federated learning, focusing on reducing communication costs, improving computational efficiency, and enhancing the security level. Federated learning is a distributed machine learning paradigm which enables model training on a large body of decentralized data. Its goal is to make full use of data across organizations or devices while meeting regulatory, privacy, and security requirements. The book starts with a self-contained introduction to artificial neural networks, deep learning models, supervised learning algorithms, evolutionary algorithms, and evolutionary learning. Concise information is then presented on multi-party secure computation, differential privacy, and homomorphic encryption, followed by a detailed description of federated learning. In turn, the book addresses the latest advances in federate learning research, especially from the perspectives of communication efficiency, evolutionarylearning, and privacy preservation.The book is particularly well suited for graduate students, academic researchers, and industrial practitioners in the field of machine learning and artificial intelligence. It can also be used as a self-learning resource for readers with a science or engineering background, or as a reference text for graduate courses.…

Zustand: New.

Paperback. Zustand: Brand New. 229 pages. 9.25x6.10x0.53 inches. In Stock.

Zustand: New. In English.

Buch. Zustand: Neu. Neuware -Harness the power of machine learning for quick and efficient calculations of protein structures and properties Machine Learning in Protein Science is a unique and practical reference that shows how to employ machine learning approaches for full quantum mechanical (FQM) calculations of protein structures and properties, thereby saving costly computing time and making this technology available for routine users. Machine Learning in Protein Science provides comprehensive coverage of topics including: - Machine learning models and algorithms, from deep neural network (DNN) and transfer learning (TL) to hybrid unsupervised and supervised learning - Protein structure predictions with AlphaFold to predict the effects of point mutations - Modeling and optimization of the catalytic activity of enzymes - Property calculations (energy, force field, stability, protein-protein interaction, thermostability, molecular dynamics) - Protein design and large language models (LLMs) of protein systems Machine Learning in Protein Science is an essential reference on the subject for biochemists, molecular biologists, theoretical chemists, biotechnologists, and medicinal chemists, as well as students in related programs of study. 240 pp. Englisch. …

Hardcover. Zustand: Brand New. 229 pages. 9.25x6.10x0.71 inches. In Stock.

Hardcover. Zustand: Brand New. 352 pages. 10.00x7.00x10.00 inches. In Stock.