A Matrix Algebra Approach To Artificial Intelligence

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A Matrix Algebra Approach to Artificial Intelligence

Xian-Da Zhang
A Matrix Algebra Approach to Artificial Intelligence Book PDF

Author : Xian-Da Zhang
Publisher : Springer Nature
Page : 844 pages
File Size : 45,9 Mb
Release : 2020-05-23
Category : Computers
ISBN : 9789811527708

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A Matrix Algebra Approach to Artificial Intelligence by Xian-Da Zhang Pdf

Matrix algebra plays an important role in many core artificial intelligence (AI) areas, including machine learning, neural networks, support vector machines (SVMs) and evolutionary computation. This book offers a comprehensive and in-depth discussion of matrix algebra theory and methods for these four core areas of AI, while also approaching AI from a theoretical matrix algebra perspective. The book consists of two parts: the first discusses the fundamentals of matrix algebra in detail, while the second focuses on the applications of matrix algebra approaches in AI. Highlighting matrix algebra in graph-based learning and embedding, network embedding, convolutional neural networks and Pareto optimization theory, and discussing recent topics and advances, the book offers a valuable resource for scientists, engineers, and graduate students in various disciplines, including, but not limited to, computer science, mathematics and engineering.

Mathematics for Machine Learning

Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Mathematics for Machine Learning Book PDF

Author : Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong
Publisher : Cambridge University Press
Page : 391 pages
File Size : 47,6 Mb
Release : 2020-04-23
Category : Computers
ISBN : 9781108470049

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Mathematics for Machine Learning by Marc Peter Deisenroth,A. Aldo Faisal,Cheng Soon Ong Pdf

Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.

Basics of Linear Algebra for Machine Learning

Jason Brownlee
Basics of Linear Algebra for Machine Learning Book PDF

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 211 pages
File Size : 47,8 Mb
Release : 2018-01-24
Category : Computers
ISBN : 8210379456XXX

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Basics of Linear Algebra for Machine Learning by Jason Brownlee Pdf

Linear algebra is a pillar of machine learning. You cannot develop a deep understanding and application of machine learning without it. In this laser-focused Ebook, you will finally cut through the equations, Greek letters, and confusion, and discover the topics in linear algebra that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover what linear algebra is, the importance of linear algebra to machine learning, vector, and matrix operations, matrix factorization, principal component analysis, and much more.

Linear Algebra And Optimization With Applications To Machine Learning - Volume I: Linear Algebra For Computer Vision, Robotics, And Machine Learning

Jean H Gallier,Jocelyn Quaintance
Linear Algebra And Optimization With Applications To Machine Learning Volume I Linear Algebra For Computer Vision Robotics And Machine Learning Book PDF

Author : Jean H Gallier,Jocelyn Quaintance
Publisher : World Scientific
Page : 823 pages
File Size : 42,7 Mb
Release : 2020-01-22
Category : Mathematics
ISBN : 9789811206412

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Linear Algebra And Optimization With Applications To Machine Learning - Volume I: Linear Algebra For Computer Vision, Robotics, And Machine Learning by Jean H Gallier,Jocelyn Quaintance Pdf

This book provides the mathematical fundamentals of linear algebra to practicers in computer vision, machine learning, robotics, applied mathematics, and electrical engineering. By only assuming a knowledge of calculus, the authors develop, in a rigorous yet down to earth manner, the mathematical theory behind concepts such as: vectors spaces, bases, linear maps, duality, Hermitian spaces, the spectral theorems, SVD, and the primary decomposition theorem. At all times, pertinent real-world applications are provided. This book includes the mathematical explanations for the tools used which we believe that is adequate for computer scientists, engineers and mathematicians who really want to do serious research and make significant contributions in their respective fields.

Linear Algebra and Optimization for Machine Learning

Charu C. Aggarwal
Linear Algebra and Optimization for Machine Learning Book PDF

Author : Charu C. Aggarwal
Publisher : Springer Nature
Page : 507 pages
File Size : 51,7 Mb
Release : 2020-05-13
Category : Computers
ISBN : 9783030403447

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Linear Algebra and Optimization for Machine Learning by Charu C. Aggarwal Pdf

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.

Introduction to Applied Linear Algebra

Stephen Boyd,Lieven Vandenberghe
Introduction to Applied Linear Algebra Book PDF

Author : Stephen Boyd,Lieven Vandenberghe
Publisher : Cambridge University Press
Page : 477 pages
File Size : 42,8 Mb
Release : 2018-06-07
Category : Business & Economics
ISBN : 9781316518960

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Introduction to Applied Linear Algebra by Stephen Boyd,Lieven Vandenberghe Pdf

A groundbreaking introduction to vectors, matrices, and least squares for engineering applications, offering a wealth of practical examples.

Nonlinear Algebra In An Acorn: With Applications To Deep Learning

Martin J Lee,Ken Kang Too Tsang
Nonlinear Algebra In An Acorn With Applications To Deep Learning Book PDF

Author : Martin J Lee,Ken Kang Too Tsang
Publisher : World Scientific
Page : 92 pages
File Size : 51,6 Mb
Release : 2018-09-05
Category : Mathematics
ISBN : 9789813271531

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Nonlinear Algebra In An Acorn: With Applications To Deep Learning by Martin J Lee,Ken Kang Too Tsang Pdf

A simple algorithm for solving a set of nonlinear equations by matrix algebra has been discovered recently — first by transforming them into an equivalent matrix equation and then finding the solution analytically in terms of the inverse matrix of this equation. With this newly developed ACORN (Adaptive Constrained Optimal Robust Nonlinear) algorithm, it is possible to minimize the objective function [constructed from the functions in the nonlinear set of equations] without computing its derivatives.This book will present the details of ACORN algorithm and how it is used to solve large scale nonlinear equations with an innovative approach ACORN Magic [minimization algorithms gathered in a cloud].The ultimate motivation of this work is its application to optimization. In recent years, with the advances in big-data, optimization becomes an even more powerful tool in knowledge discovery. ACORN Magic is the perfect choice in this kind of application because of that fact that it is fast, robust and simple enough to be embedded in any type of machine learning program.

Matrix Algebra

James E. Gentle
Matrix Algebra Book PDF

Author : James E. Gentle
Publisher : Springer Science & Business Media
Page : 536 pages
File Size : 48,7 Mb
Release : 2007-07-27
Category : Computers
ISBN : 9780387708720

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Matrix Algebra by James E. Gentle Pdf

Matrix algebra is one of the most important areas of mathematics for data analysis and for statistical theory. This much-needed work presents the relevant aspects of the theory of matrix algebra for applications in statistics. It moves on to consider the various types of matrices encountered in statistics, such as projection matrices and positive definite matrices, and describes the special properties of those matrices. Finally, it covers numerical linear algebra, beginning with a discussion of the basics of numerical computations, and following up with accurate and efficient algorithms for factoring matrices, solving linear systems of equations, and extracting eigenvalues and eigenvectors.

Practical Linear Algebra for Machine Learning

Amirsina Torfi
Practical Linear Algebra for Machine Learning Book PDF

Author : Amirsina Torfi
Publisher : Unknown
Page : 64 pages
File Size : 44,9 Mb
Release : 2019-12-26
Category : Electronic
ISBN : 1651122636

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Practical Linear Algebra for Machine Learning by Amirsina Torfi Pdf

Machine Learning is everywhere these days and a lot of fellows desire to learn it and even master it! This burning desire creates a sense of impatience. We are looking for shortcuts and willing to ONLY jump to the main concept. If you do a simple search on the web, you see thousands of people asking "How can I learn Machine Learning?", "What is the fastest approach to learn Machine Learning?", and "What are the best resources to start Machine Learning?" \textit. Mastering a branch of science is NOT just a feel-good exercise. It has its own requirements.One of the most critical requirements for Machine Learning is Linear Algebra. Basically, the majority of Machine Learning is working with data and optimization. How can you want to learn those without Linear Algebra? How would you process and represent data without vectors and matrices? On the other hand, Linear Algebra is a branch of mathematics after all. A lot of people trying to avoid mathematics or have the temptation to "just learn as necessary." I agree with the second approach, though. \textit: You cannot escape Linear Algebra if you want to learn Machine Learning and Deep Learning. There is NO shortcut.The good news is there are numerous resources out there. In fact, the availability of numerous resources made me ponder whether writing this book was necessary? I have been blogging about Machine Learning for a while and after searching and searching I realized there is a deficiency of an organized book which \textbf teaches the most used Linear Algebra concepts in Machine Learning, \textbf provides practical notions using everyday used programming languages such as Python, and \textbf be concise and NOT unnecessarily lengthy.In this book, you get all of what you need to learn about Linear Algebra that you need to master Machine Learning and Deep Learning.

Linear Algebra For Dummies

Mary Jane Sterling
Linear Algebra For Dummies Book PDF

Author : Mary Jane Sterling
Publisher : John Wiley & Sons
Page : 387 pages
File Size : 52,6 Mb
Release : 2009-06-05
Category : Mathematics
ISBN : 9780470538166

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Linear Algebra For Dummies by Mary Jane Sterling Pdf

Learn to: Solve linear algebra equations in several ways Put data in order with matrices Determine values with determinants Work with eigenvalues and eigenvectors Your hands-on guide to real-world applications of linear algebra Does linear algebra leave you feeling lost? No worries this easy-to-follow guide explains the how and the why of solving linear algebra problems in plain English. From matrices to vector spaces to linear transformations, you'll understand the key concepts and see how they relate to everything from genetics to nutrition to spotted owl extinction. Line up the basics discover several different approaches to organizing numbers and equations, and solve systems of equations algebraically or with matrices Relate vectors and linear transformations link vectors and matrices with linear combinations and seek solutions of homogeneous systems Evaluate determinants see how to perform the determinant function on different sizes of matrices and take advantage of Cramer's rule Hone your skills with vector spaces determine the properties of vector spaces and their subspaces and see linear transformation in action Tackle eigenvalues and eigenvectors define and solve for eigenvalues and eigenvectors and understand how they interact with specific matrices Open the book and find: Theoretical and practical ways of solving linear algebra problems Definitions of terms throughout and in the glossary New ways of looking at operations How linear algebra ties together vectors, matrices, determinants, and linear transformations Ten common mathematical representations of Greek letters Real-world applications of matrices and determinants

Matrix Analysis and Applications

Xian-Da Zhang
Matrix Analysis and Applications Book PDF

Author : Xian-Da Zhang
Publisher : Cambridge University Press
Page : 761 pages
File Size : 49,7 Mb
Release : 2017-10-05
Category : Computers
ISBN : 9781108417419

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Matrix Analysis and Applications by Xian-Da Zhang Pdf

The theory, methods and applications of matrix analysis are presented here in a novel theoretical framework.

Linear Algebra Problem Book

Paul R. Halmos
Linear Algebra Problem Book Book PDF

Author : Paul R. Halmos
Publisher : American Mathematical Soc.
Page : 333 pages
File Size : 51,5 Mb
Release : 1995-12-31
Category : Mathematics
ISBN : 9781614442127

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Linear Algebra Problem Book by Paul R. Halmos Pdf

Linear Algebra Problem Book can be either the main course or the dessert for someone who needs linear algebraand today that means every user of mathematics. It can be used as the basis of either an official course or a program of private study. If used as a course, the book can stand by itself, or if so desired, it can be stirred in with a standard linear algebra course as the seasoning that provides the interest, the challenge, and the motivation that is needed by experienced scholars as much as by beginning students. The best way to learn is to do, and the purpose of this book is to get the reader to DO linear algebra. The approach is Socratic: first ask a question, then give a hint (if necessary), then, finally, for security and completeness, provide the detailed answer.

Symbolic and Numerical Computation for Artificial Intelligence

Bruce R. Donald,Deepak Kapur,Joseph L. Mundy
Symbolic and Numerical Computation for Artificial Intelligence Book PDF

Author : Bruce R. Donald,Deepak Kapur,Joseph L. Mundy
Publisher : Unknown
Page : 392 pages
File Size : 44,5 Mb
Release : 1992
Category : Computers
ISBN : UOM:39015028453804

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Symbolic and Numerical Computation for Artificial Intelligence by Bruce R. Donald,Deepak Kapur,Joseph L. Mundy Pdf

Over the last decade, there has been considerable progress in investigating methods of symbolic mathematics in many application areas of computer science and artifical intelligence, such as engineering design, solid and geometric modelling, robotics and motion planning, and machine vision. This research has produced few applications within engineering and robotics because of the combinatorial cost of symbolic techniques. Therefore, it is essential to investigate approaches for systematic integration of symbolic with numerical techniques which are efficient for handling the huge amount of data that arises in practical applications, while at the same time maintain a logically consistent solution framework. Symbolic and Numerical Computation for Artificial Intelligence gives an overview of applications in machine vision, robotics and engineering design where there is a need for integrating symbolic and numerical methods. It also illustrates the case for an integrated symbolic and numerical environment to support the needs of these applications. This book will be essential reading for researchers in applied mathematics, symbolic and algebraic manipulation, and applied artificial intell

System Design for Epidemics Using Machine Learning and Deep Learning

G. R. Kanagachidambaresan,Dinesh Bhatia,Dhilip Kumar,Animesh Mishra
System Design for Epidemics Using Machine Learning and Deep Learning Book PDF

Author : G. R. Kanagachidambaresan,Dinesh Bhatia,Dhilip Kumar,Animesh Mishra
Publisher : Springer Nature
Page : 336 pages
File Size : 43,7 Mb
Release : 2023-02-01
Category : Technology & Engineering
ISBN : 9783031197529

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System Design for Epidemics Using Machine Learning and Deep Learning by G. R. Kanagachidambaresan,Dinesh Bhatia,Dhilip Kumar,Animesh Mishra Pdf

This book explores the benefits of deploying Machine Learning (ML) and Artificial Intelligence (AI) in the health care environment. The authors study different research directions that are working to serve challenges faced in building strong healthcare infrastructure with respect to the pandemic crisis. The authors take note of obstacles faced in the rush to develop and alter technologies during the Covid crisis. They study what can be learned from them and what can be leveraged efficiently. The authors aim to show how healthcare providers can use technology to exploit advances in machine learning and deep learning in their own applications. Topics include remote patient monitoring, data analysis of human behavioral patterns, and machine learning for decision making in real-time.

Math for Deep Learning

Ronald T. Kneusel
Math for Deep Learning Book PDF

Author : Ronald T. Kneusel
Publisher : No Starch Press
Page : 346 pages
File Size : 47,7 Mb
Release : 2021-11-23
Category : Computers
ISBN : 9781718501911

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Math for Deep Learning by Ronald T. Kneusel Pdf

Math for Deep Learning provides the essential math you need to understand deep learning discussions, explore more complex implementations, and better use the deep learning toolkits. With Math for Deep Learning, you'll learn the essential mathematics used by and as a background for deep learning. You’ll work through Python examples to learn key deep learning related topics in probability, statistics, linear algebra, differential calculus, and matrix calculus as well as how to implement data flow in a neural network, backpropagation, and gradient descent. You’ll also use Python to work through the mathematics that underlies those algorithms and even build a fully-functional neural network. In addition you’ll find coverage of gradient descent including variations commonly used by the deep learning community: SGD, Adam, RMSprop, and Adagrad/Adadelta.