Download e-book for iPad: Cloud Computing: Theory and Practice by Dan C. Marinescu

By Dan C. Marinescu

ISBN-10: 0124046274

ISBN-13: 9780124046276

Cloud Computing: concept and Practice presents scholars and IT execs with an in-depth research of the cloud from the floor up. starting with a dialogue of parallel computing and architectures and allotted platforms, the booklet turns to modern cloud infrastructures, how they're being deployed at prime businesses comparable to Amazon, Google and Apple, and the way they are often utilized in fields equivalent to healthcare, banking and technological know-how. the quantity additionally examines the way to effectively install a cloud program around the company utilizing virtualization, source administration and the correct quantity of networking aid, together with content material supply networks and garage sector networks. builders will discover a whole creation to program improvement supplied on various platforms.
• know about contemporary developments in cloud computing in serious parts resembling: source administration, defense, strength intake, ethics, and intricate platforms
• Get a close hands-on set of sensible recipes that support simplify the deployment of a cloud dependent process for functional use of computing clouds in addition to an in-depth dialogue of numerous projects
• comprehend the evolution of cloud computing and why the cloud computing paradigm has a greater likelihood to be triumphant than past efforts in large-scale dispensed computing

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Extra info for Cloud Computing: Theory and Practice

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2. Data set (used for model estimation): Here we distinguish between the two possibilities. In predictive learning and statistical model estimation, the data set is given explicitly. In data mining, the data set (used for obtaining a useful model) often is not given but must be extracted from a large (given) data set. The term ‘‘data mining’’ suggests that one should search for this data set (with useful properties), which is hidden somewhere in available data. 3. Formal problem statement providing (assumed) statistical model for data generation and the goal of estimation (learning).

3 provides the philosophical interpretation of learning and defines major concepts and issues necessary for understanding various adaptive methods (presented in later chapters). 1) is naturally related to the philosophical notions of induction and deduction. , knowledge outside the data) in learning is also examined. Adaptive methods achieve greater flexibility by specifying a wider class of approximating functions (than parametric methods). The predictive model is then selected from this wide class of functions.

Sometimes the frequentist interpretation does not make sense. For example, an economist predicting 80 percent chance of an interest rate cut in the near future does not really have in mind a random experiment repeated, say, 1000 times. In this case, the term probability is used to express a measure of subjective degree of belief in a particular outcome by an observer. Assuming events with disjoint outcomes (as in the frequentist interpretation), it is natural to encode subjective beliefs as real numbers between 0 and 1.

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Cloud Computing: Theory and Practice by Dan C. Marinescu

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