Pasqua D'Ambra & Mario Guarracino: Euro-Par 2010 - Parallel Processing (PDF) < الرياضيات < علوم < آخر الأخبار

Pasqua D’Ambra & Mario Guarracino
Euro-Par 2010 – Parallel Processing
16th International Euro-Par Conference, Ischia, Italy, August 31 – September 3, 2010, Proceedings, Part II

الدعم

غلاف Pasqua D'Ambra & Mario Guarracino: Euro-Par 2010 - Parallel Processing (PDF)

€56.13

لغة الإنجليزية ● شكل PDF ● ISBN 9783642152917 ● محرر Pasqua D’Ambra & Mario Guarracino ● الناشر Springer Berlin Heidelberg ● نشرت 2010 ● للتحميل 3 مرات ● دقة EUR ● هوية شخصية 6321519 ● حماية النسخ Adobe DRM

يتطلب قارئ الكتاب الاليكتروني قادرة DRM

المزيد من الكتب الإلكترونية من نفس المؤلف (المؤلفين) / محرر

Pasqua D Ambra Mario Guarracino Domenico Talia Pasqua D. Ambra

47٬429 كتب إلكترونية في هذه الفئة

غلاف Jiling (Auckland University of Technology, New Zealand) Cao & Warren B. (The University of Auckland, New Zealand) Moors: Separate and Joint Continuity

Jiling (Auckland University of Technology, New Zealand) Cao & Warren B. (The University of Auckland, New Zealand) Moors: Separate and Joint Continuity

Separate and Joint Continuity presents and summarises the main ideas and theorems that have been developed on this topic, which lies at the interface between General Topology and Functional Analysis …

غلاف COMAP: For All Practical Purposes

COMAP: For All Practical Purposes

غلاف Lucia Darrow & Walter DeGrange: Field Guide to Compelling Analytics

Lucia Darrow & Walter DeGrange: Field Guide to Compelling Analytics

Field Guide to Compelling Analytics is written for Analytics Professionals (APs) who want to increase their probability of success in implementing analytical solutions. In the past, soft skills such …

غلاف Michael J. (University of Florida, Gainesville, USA) Daniels & Antonio Linero: Bayesian Nonparametrics for Causal Inference and Missing Data

Michael J. (University of Florida, Gainesville, USA) Daniels & Antonio Linero: Bayesian Nonparametrics for Causal Inference and Missing Data

Bayesian Nonparametrics for Causal Inference and Missing Data provides an overview of flexible Bayesian nonparametric (BNP) methods for modeling joint or conditional distributions and functional rela …

غلاف Chris (University of Bath, UK) Chatfield: Problem Solving

Chris (University of Bath, UK) Chatfield: Problem Solving

This book illuminates the complex process of problem solving, including formulating the problem, collecting and analyzing data, and presenting the …

غلاف Rudolf Taschner: Numbers at Work

Rudolf Taschner: Numbers at Work

Drawing primarily from historical examples, this book explains the tremendous role that numbers and, in particular, mathematics play in all aspects of our civilization and culture. The lively style …

غلاف Martin Hugh (University of Victoria, Division of Continuing Studies, Canada) Monkman: The Data Preparation Journey

Martin Hugh (University of Victoria, Division of Continuing Studies, Canada) Monkman: The Data Preparation Journey

The Data Preparation Journey: Finding Your Way With R introduces the principles of data preparation within in a systematic approach that follows a typical data science or statistical workflow. With t …

غلاف Wentao Huang & Jibin Li: Planar Dynamical Systems

Wentao Huang & Jibin Li: Planar Dynamical Systems

In 2008, November 23-28, the workshop of ”Classical Problems on Planar Polynomial Vector Fields ” was held in the Banff International Research Station, Canada. Called ‘classical problems’, it was con …

غلاف H.R. Madala: Inductive Learning Algorithms for Complex Systems Modeling

H.R. Madala: Inductive Learning Algorithms for Complex Systems Modeling

Inductive Learning Algorithms for Complex Systems Modeling is a professional monograph that surveys new types of learning algorithms for modeling complex scientific systems in science and …

غلاف John E. Kolassa: An Introduction to Nonparametric Statistics

John E. Kolassa: An Introduction to Nonparametric Statistics

An Introduction to Nonparametric Statistics presents techniques for statistical analysis in the absence of strong assumptions about the distributions generating the data.　Rank-based and resamplin …

الصفحة التالية >>>