Fundamentals of Deep Learning: Designing Next-Generation Artificial Intelligence Algorithms, Buduma Nikhil

Автор: Buduma Nithin, Buduma Nikhil
Название: Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms, 2 ed.
ISBN: 149208218X ISBN-13(EAN): 9781492082187
Издательство: Wiley
Цена: 10136.00 р.
Наличие на складе: Есть у поставщикаПоставка под заказ.
Описание: This updated second edition describes the intuition behind deep learning innovations without jargon or complexity. By the end of this book, Python-proficient programmers, software engineering professionals, and computer science majors will be able to re-implement these breakthroughs on their own.

Автор: Kelleher John D., Macnamee Brian, D`Arcy Aoife
Название: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies
ISBN: 0262029448 ISBN-13(EAN): 9780262029445
Издательство: MIT Press
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Цена: 13543.00 р.
Наличие на складе: Нет в наличии.

Описание:

A comprehensive introduction to the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context.

After discussing the trajectory from data to insight to decision, the book describes four approaches to machine learning: information-based learning, similarity-based learning, probability-based learning, and error-based learning. Each of these approaches is introduced by a nontechnical explanation of the underlying concept, followed by mathematical models and algorithms illustrated by detailed worked examples. Finally, the book considers techniques for evaluating prediction models and offers two case studies that describe specific data analytics projects through each phase of development, from formulating the business problem to implementation of the analytics solution. The book, informed by the authors' many years of teaching machine learning, and working on predictive data analytics projects, is suitable for use by undergraduates in computer science, engineering, mathematics, or statistics; by graduate students in disciplines with applications for predictive data analytics; and as a reference for professionals.