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ISBN 10: 1032938730 ISBN 13: 9781032938738
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In den WarenkorbPaperback. Zustand: Brand New. 446 pages. 10.00x7.00x10.00 inches. In Stock.
Sprache: Englisch
Verlag: Springer International Publishing, 2021
ISBN 10: 3031037480 ISBN 13: 9783031037481
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces basic machine learning concepts and applications for a broad audience that includes students, faculty, and industry practitioners. We begin by describing how machine learning provides capabilities to computers and embedded systems to learn from data. A typical machine learning algorithm involves training, and generally the performance of a machine learning model improves with more training data. Deep learning is a sub-area of machine learning that involves extensive use of layers of artificial neural networks typically trained on massive amounts of data. Machine and deep learning methods are often used in contemporary data science tasks to address the growing data sets and detect, cluster, and classify data patterns. Although machine learning commercial interest has grown relatively recently, the roots of machine learning go back to decades ago. We note that nearly all organizations, including industry, government, defense, and health, are using machine learning toaddress a variety of needs and applications. The machine learning paradigms presented can be broadly divided into the following three categories: supervised learning, unsupervised learning, and semi-supervised learning. Supervised learning algorithms focus on learning a mapping function, and they are trained with supervision on labeled data. Supervised learning is further sub-divided into classification and regression algorithms. Unsupervised learning typically does not have access to ground truth, and often the goal is to learn or uncover the hidden pattern in the data. Through semi-supervised learning, one can effectively utilize a large volume of unlabeled data and a limited amount of labeled data to improve machine learning model performances. Deep learning and neural networks are also covered in this book. Deep neural networks have attracted a lot of interest during the last ten years due to the availability of graphics processing units (GPU) computational power, big data, and new software platforms. They have strong capabilities in terms of learning complex mapping functions for different types of data. We organize the book as follows. The book starts by introducing concepts in supervised, unsupervised, and semi-supervised learning. Several algorithms and their inner workings are presented within these three categories. We then continue with a brief introduction to artificial neural network algorithms and their properties. In addition, we cover an array of applications and provide extensive bibliography. The book ends with a summary of the key machine learning concepts.
Zustand: Sehr gut. Zustand: Sehr gut | Seiten: 112 | Sprache: Englisch | Produktart: Bücher | Machine Learning for Absolute Beginners" is a book designed to introduce readers with no prior experience to the exciting and rapidly growing field of machine learning. Machine learning is a branch of artificial intelligence that enables computers to learn from data and make predictions or decisions based on that learning.This book is written in a clear and approachable style, making it easy for readers to understand the core concepts and techniques of machine learning. It assumes no prior knowledge of the subject, and starts from the very basics, gradually building up the reader's understanding of the field.The book covers a wide range of topics, including data preprocessing, classification, regression, clustering, and deep learning. It also includes practical examples and hands-on exercises that allow readers to apply what they've learned and gain real-world experience in machine learning.Whether you are a student, a professional, or just someone interested in learning about machine learning, this book provides a solid foundation for understanding the fundamentals of this exciting field. By the end of the book, readers will have a strong understanding of the concepts and techniques of machine learning and will be well-equipped to tackle more advanced topics in the future.
Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139815991 ISBN 13: 9786139815999
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Applications of Deep Learning to Radar Polarimetry | A Physics First Approach to Machine Learning in Radar Earth Observation Applications | Shaunak de | Taschenbuch | 212 S. | Englisch | 2018 | LAP LAMBERT Academic Publishing | EAN 9786139815999 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
Sprache: Englisch
Verlag: Engineering Science Reference, 2020
ISBN 10: 1799830950 ISBN 13: 9781799830955
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Hardcover. Zustand: Very Good. No Jacket. May have limited writing in cover pages. Pages are unmarked. ~ ThriftBooks: Read More, Spend Less.
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Sprache: Englisch
Verlag: LAP LAMBERT Academic Publishing, 2018
ISBN 10: 6139815991 ISBN 13: 9786139815999
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In den WarenkorbPaperback. Zustand: Brand New. 212 pages. 8.66x5.91x0.48 inches. In Stock.
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In den WarenkorbHardcover. Zustand: Brand New. 400 pages. 9.25x6.10x0.30 inches. In Stock.
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Taschenbuch. Zustand: Neu. Variational Methods for Machine Learning with Applications to Deep Networks | Lucas Pinheiro Cinelli (u. a.) | Taschenbuch | xiv | Englisch | 2022 | Springer | EAN 9783030706814 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Sprache: Englisch
Verlag: Springer International Publishing, Springer Nature Switzerland Mai 2021, 2021
ISBN 10: 3030706788 ISBN 13: 9783030706784
Anbieter: buchversandmimpf2000, Emtmannsberg, BAYE, Deutschland
Buch. Zustand: Neu. Neuware -This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere.Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning;Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes;Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 180 pp. Englisch.