Описание: In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data.
Автор: Thomas Holton Название: Digital Signal Processing: Principles and Applications ISBN: 1108418449 ISBN-13(EAN): 9781108418447 Издательство: Cambridge Academ Рейтинг: Цена: 16474.00 р. Наличие на складе: Заказано в издательстве.
Описание: A comprehensive and mathematically accessible introduction to digital signal processing, with clear explanations of elementary principles, advanced topics, and applications. It features over 600 full-color figures, 200 worked examples, hundreds of end-of-chapter problems, and computational examples of DSP algorithms implemented in Matlab and C.
Do you Want to learn more about Python Machine Learning ?.... then read on.
Machine learning stems from this question: Can a computer go beyond anything we can order to do and learn by itself to do a specific task? Can a laptop surprise us? Instead of having programmers carefully and manually writing a set of data processing rules, can a computer automatically learn these rules by merely looking at the data?
This question paves the way for a new programming paradigm. In classical programming, on which symbolic artificial intelligence is based, human beings insert rules (the program) and the data to be processed according to these rules and obtain answers. Humans enter data and expected responses based on that data with machine learning, and the computer identifies the practices. These rules can then be applied to other data to produce different, original answers.
A machine learning system is trained and not programmed. He is presented with numerous examples relevant to a given task. In these examples, he finds a statistical structure that ultimately allows him to produce the rules for the task's automation. For example, to automate tagging vacation photographs, many examples of images already tagged by humans could be presented to a machine learning system. The system would be tasked with learning the statistical rules based on associating individual images with specific tags.
Machine learning is closely related to statistics, but it differs from them in many important ways. Unlike statistics, machine learning tends to operate with large and complex datasets (such as a dataset of millions of images, each consisting of tens of thousands of pixels) for which classical statistical analysis such as Bayesian analysis would not be usable. . As a result, machine learning, and especially deep learning, exhibits somewhat limited mathematical theory - sometimes too much - and is more technical than mathematical. It is a practical discipline in which ideas often prove more empirically than theoretical.
In this Book you will learning:
What is Data Science and Deep Learning?
Data Science and Applications
Probability - Fundamental - Statistics
Understanding the Fundamentals of iMachine Learning
Types of MachineiLearning
What is iPython? SettingiUp the Environment in Python
K - Nearest Neighbor Algorithms
Means Clustering
Neural Networks - Linear Classifiers
While most books focus on advanced predictive models, this book begins to explain the basic concepts and how to correctly implement Data Science and Machine Learning, with practical examples and simple coding scripts.
This guide provides the necessary knowledge in a practical way. You will learn the steps of Machine Learning, how to implement them in Python, and the most important applications in the real world.
Would you like to know more?
Download the Book, Python Machine Learning.
Scroll to the top of the page and click the "Buy now" button to get your copy now.
Do you Want to learn more about Python Machine Learning ?.... then read on.
Machine learning stems from this question: Can a computer go beyond anything we can order to do and learn by itself to do a specific task? Can a laptop surprise us? Instead of having programmers carefully and manually writing a set of data processing rules, can a computer automatically learn these rules by merely looking at the data?
This question paves the way for a new programming paradigm. In classical programming, on which symbolic artificial intelligence is based, human beings insert rules (the program) and the data to be processed according to these rules and obtain answers. Humans enter data and expected responses based on that data with machine learning, and the computer identifies the practices. These rules can then be applied to other data to produce different, original answers.
A machine learning system is trained and not programmed. He is presented with numerous examples relevant to a given task. In these examples, he finds a statistical structure that ultimately allows him to produce the rules for the task's automation. For example, to automate tagging vacation photographs, many examples of images already tagged by humans could be presented to a machine learning system. The system would be tasked with learning the statistical rules based on associating individual images with specific tags.
Machine learning is closely related to statistics, but it differs from them in many important ways. Unlike statistics, machine learning tends to operate with large and complex datasets (such as a dataset of millions of images, each consisting of tens of thousands of pixels) for which classical statistical analysis such as Bayesian analysis would not be usable. . As a result, machine learning, and especially deep learning, exhibits somewhat limited mathematical theory - sometimes too much - and is more technical than mathematical. It is a practical discipline in which ideas often prove more empirically than theoretical.
In this Book you will learning:
What is Data Science and Deep Learning?
Data Science and Applications
Probability - Fundamental - Statistics
Understanding the Fundamentals of iMachine Learning
Types of MachineiLearning
What is iPython? SettingiUp the Environment in Python
K - Nearest Neighbor Algorithms
Means Clustering
Neural Networks - Linear Classifiers
While most books focus on advanced predictive models, this book begins to explain the basic concepts and how to correctly implement Data Science and Machine Learning, with practical examples and simple coding scripts.
This guide provides the necessary knowledge in a practical way. You will learn the steps of Machine Learning, how to implement them in Python, and the most important applications in the real world.
Would you like to know more?
Download the Book, Python Machine Learning.
Scroll to the top of the page and click the "Buy now" button to get your copy now.
Описание: Thisintroductory book discusses how to plan and build useful, reliable,maintainable and cost efficient computer systems for automated engineeringdesign.
Описание: Brain Storm Optimization (BSO) algorithms are a new kind of swarm intelligence method, which is based on the collective behavior of human beings, i.e., on the brainstorming process.
Описание: This book provides a comprehensive introduction to rough set-based feature selection. Rough set theory, first proposed by Zdzislaw Pawlak in 1982, continues to evolve. Concerned with the classification and analysis of imprecise or uncertain information and knowledge, it has become a prominent tool for data analysis, and enables the reader to systematically study all topics in rough set theory (RST) including preliminaries, advanced concepts, and feature selection using RST. The book is supplemented with an RST-based API library that can be used to implement several RST concepts and RST-based feature selection algorithms.The book provides an essential reference guide for students, researchers, and developers working in the areas of feature selection, knowledge discovery, and reasoning with uncertainty, especially those who are working in RST and granular computing. The primary audience of this book is the research community using rough set theory (RST) to perform feature selection (FS) on large-scale datasets in various domains. However, any community interested in feature selection such as medical, banking, and finance can also benefit from the book. This second edition also covers the dominance-based rough set approach and fuzzy rough sets. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle. In turn, fuzzy rough sets are fuzzy generalizations of rough sets. An API library for the DRSA is also provided with the second edition of the book.
Introduction to Feature Selection.- Background.- Rough Set Theory.- Advance Concepts in RST.- Rough Set Based Feature Selection Techniques.- Unsupervised Feature Selection using RST.- Critical Analysis of Feature Selection Algorithms.- RST Source Code.
Описание: This book provides a comprehensive introduction to rough set-based feature selection. The dominance-based rough set approach (DRSA) is an extension of the conventional rough set approach and supports the preference order using the dominance principle.
Описание: The three-volume set LNCS 12476 - 12478 constitutes the refereed proceedings of the 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, which was planned to take place during October 20-30, 2020, on Rhodes, Greece.
Описание: This must-read text/reference provides a practical guide to processes involved in the development and application of dynamic simulation models, covering a wide range of issues relating to testing, verification and validation. Illustrative example problems in continuous system simulation are presented throughout the book, supported by extended case studies from a number of interdisciplinary applications. Topics and features: provides an emphasis on practical issues of model quality and validation, along with questions concerning the management of simulation models, the use of model libraries, and generic models; contains numerous step-by-step examples; presents detailed case studies, often with accompanying datasets; includes discussion of hybrid models, which involve a combination of continuous system and discrete-event descriptions; examines experimental modeling approaches that involve system identification and parameter estimation; offers supplementary material at an associated website.
Описание: The three-volume set LNCS 12476 - 12478 constitutes the refereed proceedings of the 9th International Symposium on Leveraging Applications of Formal Methods, ISoLA 2020, which was planned to take place during October 20-30, 2020, on Rhodes, Greece.
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