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The recommendations on evaluating and improving algorithm reliability for shortening the development cycle of deep learning algorithms and improving the quality of software systems based on deep learning algorithms are defined in this document. An assessment index system and corresponding assessment process are specified in this document.
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Algorithms are probably the most sophisticated tools that people have had at their disposal since the beginnings of human history. They have transformed science, industry, society. They upset the concepts of work, property, government, private life, even humanity. Going easily from one extreme to the other, we rejoice that they make life easier for us, but fear that they will enslave us. To get beyond this vision of good vs evil, this book takes a new look at our time, the age of algorithms. Creations of the human spirit, algorithms are what we made them. And they will be what we want them to be: it's up to us to choose the world we want to live in.
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The text covers important algorithm design techniques, such as greedy algorithms, dynamic programming, and divide-and-conquer, and gives applications to contemporary problems. Techniques including Fast Fourier transform, KMP algorithm for string matching, CYK algorithm for context free parsing and gradient descent for convex function minimization are discussed in detail. The book's emphasis is on computational models and their effect on algorithm design. It gives insights into algorithm design techniques in parallel, streaming and memory hierarchy computational models. The book also emphasizes the role of randomization in algorithm design, and gives numerous applications ranging from data-structures such as skip-lists to dimensionality reduction methods.
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This book presents some of the emerging evolutionary algorithms (EAs) and their variants. It presents design cases of different EAs applied to popular design problems in antennas and wireless communications. The book contains both cases of single and multi-objective optimization.
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Contains 35 contributions discussing current techniques in scientific visualization, which involves using computer-generated pictures to gain information and knowledge from data and relationships. Intended both as an overview for the inquiring scientist and as a foundation for developers, this book contains chapters dedicated to surveys and tutorials of specific topics, as well as previously unpublished work. Some recurring themes amongst the contributions are vector and tensor field visualizations; volume visualization, interactive steering and exploration;and large data sets and multiresolution techniques. Lacks a subject index. Annotation copyrighted by Book News, Inc., Portland, OR.
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