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Practical mathematics for AI and deep learning

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Practical mathematics for AI and deep learning/ Tamoghna Ghosh, Shravan Kumar Belagal Math.
作者:	Ghosh, Tamoghna.
其他作者:	Math, Shravan Kumar Belagal,
面頁冊數:	1 online resource (530 pages)
標題:	COMPUTERS / Data Science / Machine Learning. -
電子資源:	https://portal.igpublish.com/iglibrary/search/BPB0000388.html
ISBN:	9789355511935

Practical mathematics for AI and deep learning
Ghosh, Tamoghna.

Practical mathematics for AI and deep learning[electronic resource] /Tamoghna Ghosh, Shravan Kumar Belagal Math. - 1 online resource (530 pages)

Includes bibliographical references and index.

Practical mathematics for AI and deep learning -- About the Authors -- About the Reviewer -- Preface -- Errata -- Table of Contents -- Chapter 1 Overview of AI -- Chapter 2 Linear Algebra -- Chapter 3 Vector Calculus -- Chapter 4 Basic Statistics and Probability Theory -- Chapter 5 Statistical Inference and Applications -- Chapter 6 Neural Networks -- Chapter 7 Clustering -- Chapter 8 Dimensionality Reduction -- Chapter 9 Computer Vision -- Chapter 10 Sequence Learning Models -- Chapter 11 Natural Language Processing -- Chapter 12 Generative Models -- Index.

Access restricted to authorized users and institutions.

To construct a system that may be referred to as having ‘Artificial Intelligence,’ it is important to develop the capacity to design algorithms capable of performing data-based automated decision-making in conditions of uncertainty. Now, to accomplish this goal, one needs to have an in-depth understanding of the more sophisticated components of linear algebra, vector calculus, probability, and statistics. This book walks you through every mathematical algorithm, as well as its architecture, its operation, and its design so that you can understand how any artificial intelligence system operates. This book will teach you the common terminologies used in artificial intelligence such as models, data, parameters of models, and dependent and independent variables. The Bayesian linear regression, the Gaussian mixture model, the stochastic gradient descent, and the backpropagation algorithms are explored with implementation beginning from scratch. The vast majority of the sophisticated mathematics required for complicated AI computations such as autoregressive models, cycle GANs, and CNN optimization are explained and compared. You will acquire knowledge that extends beyond mathematics while reading this book. Specifically, you will become familiar with numerous AI training methods, various NLP tasks, and the process of reducing the dimensionality of data.

Mode of access: World Wide Web.

ISBN: 9789355511935Subjects--Topical Terms:

1483854
COMPUTERS / Data Science / Machine Learning.
Index Terms--Genre/Form:

554714
Electronic books.

LC Class. No.: Q334.7

Dewey Class. No.: 006.3

Practical mathematics for AI and deep learning
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506 $a Access restricted to authorized users and institutions.
520 3 $a To construct a system that may be referred to as having ‘Artificial Intelligence,’ it is important to develop the capacity to design algorithms capable of performing data-based automated decision-making in conditions of uncertainty. Now, to accomplish this goal, one needs to have an in-depth understanding of the more sophisticated components of linear algebra, vector calculus, probability, and statistics. This book walks you through every mathematical algorithm, as well as its architecture, its operation, and its design so that you can understand how any artificial intelligence system operates. This book will teach you the common terminologies used in artificial intelligence such as models, data, parameters of models, and dependent and independent variables. The Bayesian linear regression, the Gaussian mixture model, the stochastic gradient descent, and the backpropagation algorithms are explored with implementation beginning from scratch. The vast majority of the sophisticated mathematics required for complicated AI computations such as autoregressive models, cycle GANs, and CNN optimization are explained and compared. You will acquire knowledge that extends beyond mathematics while reading this book. Specifically, you will become familiar with numerous AI training methods, various NLP tasks, and the process of reducing the dimensionality of data.
538 $a Mode of access: World Wide Web.
650 7 $a COMPUTERS / Data Science / Machine Learning. $2 bisacsh $3 1483854
650 7 $a COMPUTERS / Data Science / Neural Networks. $2 bisacsh $3 1483844
650 7 $a COMPUTERS / Artificial Intelligence / Computer Vision & Pattern Recognition. $2 bisacsh $3 1483850
655 4 $a Electronic books. $2 local $3 554714
700 1 $a Math, Shravan Kumar Belagal, $e author $3 1483889
856 4 0 $u https://portal.igpublish.com/iglibrary/search/BPB0000388.html