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Condizione: New. pp. 68.
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Lingua: Inglese
Editore: Spektrum Akademischer Verlag Gmbh, 2016
ISBN 10: 365812878X ISBN 13: 9783658128784
Da: Revaluation Books, Exeter, Regno Unito
EUR 74,99
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Aggiungi al carrelloPaperback. Condizione: Brand New. 84 pages. 9.25x6.10x0.19 inches. In Stock.
Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden, 2016
ISBN 10: 365812878X ISBN 13: 9783658128784
Da: moluna, Greven, Germania
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Editore: Springer Fachmedien Wiesbaden, Springer Fachmedien Wiesbaden, 2016
ISBN 10: 365812878X ISBN 13: 9783658128784
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 53,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Manuel Kroiss examines the differentiation of hematopoietic stem cells using machine learning methods. This work is based on experiments focusing on the lineage choice of CMPs, the progenitors of HSCs, which either become MEP or GMP cells. The author presents a novel approach to distinguish MEP from GMP cells using machine learning on morphology features extracted from bright field images. He tests the performance of different models and focuses on Recurrent Neural Networks with the latest advances from the field of deep learning. Two different improvements to recurrent networks were tested: Long Short Term Memory (LSTM) cells that are able to remember information over long periods of time, and dropout regularization to prevent overfitting. With his method, Manuel Kroiss considerably outperforms standard machine learning methods without time information like Random Forests and Support Vector Machines.
Lingua: Tedesco
Editore: Baden-Baden, Nomos (Lizenzausgabe Zerb Verlag), 2005
ISBN 10: 3832912444 ISBN 13: 9783832912444
Da: Antiquariat Dennis R. Plummer, Bingen am Rhein, Germania
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Aggiungi al carrelloGr.-8°, Original-Pappband. Condizione: Gut. LXVIII, 996 SS. NomosProzessHandbuch. - Sauber und gut erhalten. Sprache: Deutsch Gewicht in Gramm: 1700.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Predicting the Lineage Choice of Hematopoietic Stem Cells | A Novel Approach Using Deep Neural Networks | Manuel Kroiss | Taschenbuch | BestMasters | xv | Englisch | 2016 | Springer Vieweg | EAN 9783658128784 | Verantwortliche Person für die EU: Springer Spektrum in Springer Science + Business Media, Tiergartenstr. 15-17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Aggiungi al carrelloPappband. 2. Auflage. 2005. LXVIII, 996 S. Opp. Neupreis 98,00, (Recht). Buch.
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EUR 169,39
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Aggiungi al carrelloHardcover. Condizione: Brand New. 6th edition. 1125 pages. German language. 9.45x6.65x2.01 inches. In Stock.
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Da: Majestic Books, Hounslow, Regno Unito
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 68.
Lingua: Inglese
Editore: Springer Fachmedien Wiesbaden Mai 2016, 2016
ISBN 10: 365812878X ISBN 13: 9783658128784
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 53,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Manuel Kroiss examines the differentiation of hematopoietic stem cells using machine learning methods. This work is based on experiments focusing on the lineage choice of CMPs, the progenitors of HSCs, which either become MEP or GMP cells. The author presents a novel approach to distinguish MEP from GMP cells using machine learning on morphology features extracted from bright field images. He tests the performance of different models and focuses on Recurrent Neural Networks with the latest advances from the field of deep learning. Two different improvements to recurrent networks were tested: Long Short Term Memory (LSTM) cells that are able to remember information over long periods of time, and dropout regularization to prevent overfitting. With his method, Manuel Kroiss considerably outperforms standard machine learning methods without time information like Random Forests and Support Vector Machines. 84 pp. Englisch.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 70,79
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 68.
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 53,49
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Manuel Kroiss examines the differentiation of hematopoietic stem cells using machine learning methods. This work is based on experiments focusing on the lineage choice of CMPs, the progenitors of HSCs, which either become MEP or GMP cells. The author presents a novel approach to distinguish MEP from GMP cells using machine learning on morphology features extracted from bright field images. He tests the performance of different models and focuses on Recurrent Neural Networks with the latest advances from the field of deep learning. Two different improvements to recurrent networks were tested: Long Short Term Memory (LSTM) cells that are able to remember information over long periods of time, and dropout regularization to prevent overfitting. With his method, Manuel Kroiss considerably outperforms standard machine learning methods without time information like Random Forests and Support Vector Machines.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 84 pp. Englisch.