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Deep Learning Architectures

2020-02-13 Mathematics
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Release : 2020-02-13
Genre : Mathematics
Kind : eBook
Book Rating : 215/5 ( reviews)

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Book Synopsis Deep Learning Architectures by : Ovidiu Calin

Download or read book Deep Learning Architectures written by Ovidiu Calin. This book was released on 2020-02-13. Available in PDF, EPUB and Kindle. Book excerpt: This book describes how neural networks operate from the mathematical point of view. As a result, neural networks can be interpreted both as function universal approximators and information processors. The book bridges the gap between ideas and concepts of neural networks, which are used nowadays at an intuitive level, and the precise modern mathematical language, presenting the best practices of the former and enjoying the robustness and elegance of the latter. This book can be used in a graduate course in deep learning, with the first few parts being accessible to senior undergraduates. In addition, the book will be of wide interest to machine learning researchers who are interested in a theoretical understanding of the subject.

Math and Architectures of Deep Learning

2024-03-26 Computers
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Release : 2024-03-26
Genre : Computers
Kind : eBook
Book Rating : 481/5 ( reviews)

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Book Synopsis Math and Architectures of Deep Learning by : Krishnendu Chaudhury

Download or read book Math and Architectures of Deep Learning written by Krishnendu Chaudhury. This book was released on 2024-03-26. Available in PDF, EPUB and Kindle. Book excerpt: Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. You'll peer inside the "black box" to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. You'll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. You'll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, you'll be glad you can quickly identify and fix problems.

Development and Analysis of Deep Learning Architectures

2019-11-01 Technology & Engineering
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Release : 2019-11-01
Genre : Technology & Engineering
Kind : eBook
Book Rating : 641/5 ( reviews)

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Book Synopsis Development and Analysis of Deep Learning Architectures by : Witold Pedrycz

Download or read book Development and Analysis of Deep Learning Architectures written by Witold Pedrycz. This book was released on 2019-11-01. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.

Learning Deep Architectures for AI

2009 Computational learning theory
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Release : 2009
Genre : Computational learning theory
Kind : eBook
Book Rating : 941/5 ( reviews)

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Book Synopsis Learning Deep Architectures for AI by : Yoshua Bengio

Download or read book Learning Deep Architectures for AI written by Yoshua Bengio. This book was released on 2009. Available in PDF, EPUB and Kindle. Book excerpt: Theoretical results suggest that in order to learn the kind of complicated functions that can represent high-level abstractions (e.g. in vision, language, and other AI-level tasks), one may need deep architectures. Deep architectures are composed of multiple levels of non-linear operations, such as in neural nets with many hidden layers or in complicated propositional formulae re-using many sub-formulae. Searching the parameter space of deep architectures is a difficult task, but learning algorithms such as those for Deep Belief Networks have recently been proposed to tackle this problem with notable success, beating the state-of-the-art in certain areas. This paper discusses the motivations and principles regarding learning algorithms for deep architectures, in particular those exploiting as building blocks unsupervised learning of single-layer models such as Restricted Boltzmann Machines, used to construct deeper models such as Deep Belief Networks.

Deep Learning: Concepts and Architectures

2019-10-29 Technology & Engineering
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Release : 2019-10-29
Genre : Technology & Engineering
Kind : eBook
Book Rating : 560/5 ( reviews)

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Book Synopsis Deep Learning: Concepts and Architectures by : Witold Pedrycz

Download or read book Deep Learning: Concepts and Architectures written by Witold Pedrycz. This book was released on 2019-10-29. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to the fundamental concepts of deep learning and offers practical insights into how this learning paradigm supports automatic mechanisms of structural knowledge representation. It discusses a number of multilayer architectures giving rise to tangible and functionally meaningful pieces of knowledge, and shows how the structural developments have become essential to the successful delivery of competitive practical solutions to real-world problems. The book also demonstrates how the architectural developments, which arise in the setting of deep learning, support detailed learning and refinements to the system design. Featuring detailed descriptions of the current trends in the design and analysis of deep learning topologies, the book offers practical guidelines and presents competitive solutions to various areas of language modeling, graph representation, and forecasting.

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