Verlag: Golden Pr (edition ), 1981
ISBN 10: 0307132013 ISBN 13: 9780307132017
Anbieter: BooksRun, Philadelphia, PA, USA
Hardcover. Zustand: Fair. Campana, Manny (illustrator). Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
Verlag: Golden Pr (edition ), 1981
ISBN 10: 0307132013 ISBN 13: 9780307132017
Anbieter: BooksRun, Philadelphia, PA, USA
Hardcover. Zustand: Good. Campana, Manny (illustrator). Ship within 24hrs. Satisfaction 100% guaranteed. APO/FPO addresses supported.
Verlag: Golden Pr, 1981
ISBN 10: 0307132013 ISBN 13: 9780307132017
Zustand: Good. Campana, Manny (illustrator). Good condition. A copy that has been read but remains intact. May contain markings such as bookplates, stamps, limited notes and highlighting, or a few light stains.
Verlag: Darton, Longman & Todd Ltd, 1964
Anbieter: World of Rare Books, Goring-by-Sea, SXW, Vereinigtes Königreich
Zustand: Good. 1964. First Published. 194 pages. Pictorial dust jacket over brown cloth. Binding remains firm. Pages have light tanning and foxing throughout. Boards have light shelf-wear with corner bumping. Very slight crushing to spine ends. Unclipped jacket has heavy edgewear with areas of loss, heavy tears, chips, and creasing. Light tanning to spine and edges. Pen inscription to rear flap. Wear marks overall.
Verlag: Scholastic
Anbieter: WeBuyBooks, Rossendale, LANCS, Vereinigtes Königreich
Zustand: Good. Most items will be dispatched the same or the next working day. Ex library copy with usual stamps & stickers.
Verlag: LIGHTNING SOURCE INC, 2016
ISBN 10: 1358089159 ISBN 13: 9781358089152
Anbieter: moluna, Greven, Deutschland
Gebunden. Zustand: New.
Verlag: John Wiley & Sons, 2021
ISBN 10: 1119408334 ISBN 13: 9781119408338
Anbieter: moluna, Greven, Deutschland
Gebunden. Zustand: New. Dr Edward O. Pyzer-Knapp is the worldwide lead for AI Enriched Modelling and Simulation at IBM Research. Previously, he obtained his PhD from the University of Cambridge using state of the art computational techniques to accelerate materials design then mov.
Verlag: Wiley & Sons, Wiley, 2021
ISBN 10: 1119408334 ISBN 13: 9781119408338
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. Neuware - Discover the power of machine learning in the physical sciences with this one-stop resource from a leading voice in the fieldDeep Learning for Physical Scientists: Accelerating Research with Machine Learning delivers an insightful analysis of the transformative techniques being used in deep learning within the physical sciences. The book offers readers the ability to understand, select, and apply the best deep learning techniques for their individual research problem and interpret the outcome.Designed to teach researchers to think in useful new ways about how to achieve results in their research, the book provides scientists with new avenues to attack problems and avoid common pitfalls and problems. Practical case studies and problems are presented, giving readers an opportunity to put what they have learned into practice, with exemplar coding approaches provided to assist the reader.From modelling basics to feed-forward networks, the book offers a broad cross-section of machine learning techniques to improve physical science research. Readers will also enjoy:\* A thorough introduction to the basic classification and regression with perceptrons\* An exploration of training algorithms, including back propagation and stochastic gradient descent and the parallelization of training\* An examination of multi-layer perceptrons for learning from descriptors and de-noising data\* Discussions of recurrent neural networks for learning from sequences and convolutional neural networks for learning from images\* A treatment of Bayesian optimization for tuning deep learning architecturesPerfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. Perfect for academic and industrial research professionals in the physical sciences, Deep Learning for Physical Scientists: Accelerating Research with Machine Learning will also earn a place in the libraries of industrial researchers who have access to large amounts of data but have yet to learn the techniques to fully exploit that access. This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: \*Basic classification and regression with perceptrons \*Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training \*Multi-Layer Perceptrons for learning from descriptors, and de-noising data \*Recurrent neural networks for learning from sequences \*Convolutional neural networks for learning from images \*Bayesian optimization for tuning deep learning architectures Each of these areas has direct application to physical science research, and by the end of the book, the reader should feel comfortable enough to select the methodology which is best for their situation, and be able to implement and interpret outcome of the deep learning model. The book is designed to teach researchers to think in new ways, providing them with new avenues to attack problems, and avoid roadblocks within their research. This is achieved through the inclusion of case-study like problems at the end of each chapter, which will give the reader a chance to practice what they have just learnt in a close-to-real-world setting, with example 'solutions' provided through an online resource. Market Description This book introduces the reader to the transformative techniques involved in deep learning. A range of methodologies are addressed including: \* Basic classification and regression with perceptrons \* Training algorithms, such as back propagation and stochastic gradient descent and the parallelization of training \* Multi-Layer Perceptrons for learning from desc.