Automatic Quantum Computer Programming, Lee Spector

Автор: Barber
Название: Bayesian Reasoning and Machine Learning
ISBN: 0521518148 ISBN-13(EAN): 9780521518147
Издательство: Cambridge Academ
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Описание: This practical introduction for final-year undergraduate and graduate students is ideally suited to computer scientists without a background in calculus and linear algebra. Numerous examples and exercises are provided. Additional resources available online and in the comprehensive software package include computer code, demos and teaching materials for instructors.

Название: Genetic Algorithms and Genetic Programming
ISBN: 1584886293 ISBN-13(EAN): 9781584886297
Издательство: Taylor&Francis
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Описание: Describes several generic algorithmic concepts that can be used in various kinds of GA or with evolutionary optimization techniques. This title provides a better understanding of the basic workflow of GAs and GP, encouraging readers to establish new bionic, problem-independent theoretical concepts.

Автор: Markus F. Brameier; Wolfgang Banzhaf
Название: Linear Genetic Programming
ISBN: 1441940480 ISBN-13(EAN): 9781441940483
Издательство: Springer
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Цена: 23058.00 р.
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Описание: Linear Genetic Programming presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees.

Автор: Marsland
Название: Machine Learning
ISBN: 1466583282 ISBN-13(EAN): 9781466583283
Издательство: Taylor&Francis
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Цена: 12095.00 р.
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Описание:

A Proven, Hands-On Approach for Students without a Strong Statistical Foundation

Since the best-selling first edition was published, there have been several prominent developments in the field of machine learning, including the increasing work on the statistical interpretations of machine learning algorithms. Unfortunately, computer science students without a strong statistical background often find it hard to get started in this area.

Remedying this deficiency, Machine Learning: An Algorithmic Perspective, Second Edition helps students understand the algorithms of machine learning. It puts them on a path toward mastering the relevant mathematics and statistics as well as the necessary programming and experimentation.

New to the Second Edition

• Two new chapters on deep belief networks and Gaussian processes
• Reorganization of the chapters to make a more natural flow of content
• Revision of the support vector machine material, including a simple implementation for experiments
• New material on random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization for the multi-layer perceptron
• Additional discussions of the Kalman and particle filters
• Improved code, including better use of naming conventions in Python

Suitable for both an introductory one-semester course and more advanced courses, the text strongly encourages students to practice with the code. Each chapter includes detailed examples along with further reading and problems. All of the code used to create the examples is available on the author's website.

Автор: Karthik S., Paul Anand, Karthikeyan N.
Название: Deep Learning Innovations and Their Convergence with Big Data
ISBN: 1522530150 ISBN-13(EAN): 9781522530152
Издательство: Mare Nostrum (Eurospan)
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Цена: 29938.00 р.
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Описание: The expansion of digital data has transformed various sectors of business such as healthcare, industrial manufacturing, and transportation. A new way of solving business problems has emerged through the use of machine learning techniques in conjunction with big data analytics.Deep Learning Innovations and Their Convergence With Big Data is a pivotal reference for the latest scholarly research on upcoming trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. Featuring extensive coverage on a broad range of topics and perspectives such as deep neural network, domain adaptation modeling, and threat detection, this book is ideally designed for researchers, professionals, and students seeking current research on the latest trends in the field of deep learning techniques in big data analytics.Contents include:Deep Auto-EncodersDeep Neural NetworkDomain Adaptation ModelingMultilayer Perceptron (MLP)Natural Language Processing (NLP)Restricted Boltzmann Machines (RBM)Threat Detection

Автор: Shalev-Shwartz
Название: Understanding Machine Learning
ISBN: 1107057132 ISBN-13(EAN): 9781107057135
Издательство: Cambridge Academ
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Цена: 11194.00 р.
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Описание: Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the `hows` and `whys` of machine-learning algorithms, making the field accessible to both students and practitioners.

Автор: Ajith Abraham
Название: Genetic Systems Programming
ISBN: 3642067530 ISBN-13(EAN): 9783642067532
Издательство: Springer
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Цена: 23757.00 р.
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Описание: Designing complex programs such as operating systems, compilers, filing systems, data base systems, etc. These include but are not limited to, information security systems, compilers, data mining systems, stock market prediction systems, robots and automatic programming.