Agarwal deepak (139 Ergebnisse)

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Hardback. Zustand: Good. Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.…

Zustand: New. pp. 448.

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Paperback. Zustand: New.

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Zustand: New. pp. 400 Figure, Illus.

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Zustand: New. pp. 446.

Zustand: New. pp. x + 298.

Zustand: New. pp. 400.

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Zustand: As New. Unread book in perfect condition.

Zustand: As New. Unread book in perfect condition.

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paperback. Zustand: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned.…

Zustand: As New. Unread book in perfect condition.

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Zustand: New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

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Hardcover. Zustand: Good. Very well maintained. Small signs of wear but no tears, scratches or major flaws. Designing algorithms to recommend items such very goods articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with. Copyright © Cambridge University Press.…

Hardcover. Zustand: New. Contents: 1. Buffer Preparation 2. Genomic DNA Isolation 3. Salting Out Method of DNA Isolation 4. Plant DNA Isolation (CTAB method) 5. DNA Isolation from Commercial Kit 6. Total RNA Isolation 7. cDNA Synthesis 8. Quantification of Nucleic Acids 9. Agarose Gel Electrophoresis 10. Polymerase Chain Reaction (PCR) 11. Restrition Endonuclease Enzymes 12. Recombinant DNA Technology 13. DNA Amplification Using RAPD 14. Protein Analysis (SDS-PAGE) 15. Enzyme Linked Immunosorbent Assay (ELISA) 16. Basic Bioinformatics.…

Hardback. Zustand: New. Designing algorithms to recommend items such as news articles and movies to users is a challenging task in numerous web applications. The crux of the problem is to rank items based on users' responses to different items to optimize for multiple objectives. Major technical challenges are high dimensional prediction with sparse data and constructing high dimensional sequential designs to collect data for user modeling and system design. This comprehensive treatment of the statistical issues that arise in recommender systems includes detailed, in-depth discussions of current state-of-the-art methods such as adaptive sequential designs (multi-armed bandit methods), bilinear random-effects models (matrix factorization) and scalable model fitting using modern computing paradigms like MapReduce. The authors draw upon their vast experience working with such large-scale systems at Yahoo! and LinkedIn, and bridge the gap between theory and practice by illustrating complex concepts with examples from applications they are directly involved with.…