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Ajouter au panierhardcover. Etat : Very Good. Very Good Condition - May show some limited signs of wear and may have a remainder mark. Pages and dust cover are intact and not marred by notes or highlighting.
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Ajouter au panierEtat : New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.
Langue: anglais
Edité par The MIT Press Bookstore, 2018
ISBN 10 : 0262039508 ISBN 13 : 9780262039505
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Ajouter au panierEtat : New.
Langue: anglais
Edité par The MIT Press Bookstore, 2018
ISBN 10 : 0262039508 ISBN 13 : 9780262039505
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Edité par The MIT Press Bookstore, 2018
ISBN 10 : 0262039508 ISBN 13 : 9780262039505
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Edité par Mit Press, 2022
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Ajouter au panierHardcover. Etat : New. ISBN:9780262039505.
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Ajouter au panierPAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.
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Ajouter au panierPAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.
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Ajouter au panierHardcover. Etat : New. Brand New! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 7-12 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
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Ajouter au panierhardcover. Etat : New. New. book.
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Ajouter au panierPaperback. Etat : New.
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Ajouter au panierPaperback. Etat : Brand New. 214 pages. 10.00x8.00x10.00 inches. In Stock.
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Ajouter au panierPaperback. Etat : New.
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Ajouter au panierPaperback / softback. Etat : New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days.
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Ajouter au panierPaperback. Etat : new. Paperback. Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.Log-linear models play a key role in modern big data and machine learning applications. From simple binary classification models through partition functions, conditional random fields, and neural nets, log-linear structure is closely related to performance in certain applications and influences fitting techniques used to train models. This volume covers recent advances in training models with log-linear structures, covering the underlying geometry, optimization techniques, and multiple applications. The first chapter shows readers the inner workings of machine learning, providing insights into the geometry of log-linear and neural net models. The other chapters range from introductory material to optimization techniques to involved use cases. The book, which grew out of a NIPS workshop, is suitable for graduate students doing research in machine learning, in particular deep learning, variable selection, and applications to speech recognition. The contributors come from academia and industry, allowing readers to view the field from both perspectives.ContributorsAleksandr Aravkin, Avishy Carmi, Guillermo A. Cecchi, Anna Choromanska, Li Deng, Xinwei Deng, Jean Honorio, Tony Jebara, Huijing Jiang, Dimitri Kanevsky, Brian Kingsbury, Fabrice Lambert, Aurelie C. Lozano, Daniel Moskovich, Yuriy S. Polyakov, Bhuvana Ramabhadran, Irina Rish, Dimitris Samaras, Tara N. Sainath, Hagen Soltau, Serge F. Timashev, Ewout van den Berg This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
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Ajouter au panierTaschenbuch. Etat : Neu. Log-Linear Models, Extensions, and Applications | Aleksandr Aravkin (u. a.) | Taschenbuch | Englisch | 2024 | MIT Press | EAN 9780262553469 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Ajouter au panierTaschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Advances in training models with log-linear structures, with topics including variable selection, the geometry of neural nets, and applications.