[PDF] Linear Algebra And Optimization With Applications To Machine Learning Volume I Linear Algebra For Computer Vision Robotics And Machine Learning

Linear Algebra and Optimization with Applications to Machine Learning   Volume I  Linear Algebra for Computer Vision  Robotics  and Machine Learning PDF
Author: Jean H. Gallier
Publisher: World Scientific Publishing Company
ISBN: 9789811207716
Size: 49.90 MB
Format: PDF, ePub
Category : Mathematics
Languages : en
Pages : 550
View: 440

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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, release date 2020-01-15, Linear Algebra And Optimization With Applications To Machine Learning Volume I Linear Algebra For Computer Vision Robotics And Machine Learning Books available in PDF, EPUB, Mobi Format. Download Linear Algebra And Optimization With Applications To Machine Learning Volume I Linear Algebra For Computer Vision Robotics And Machine Learning books, 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.




[PDF] Linear Algebra And Optimization With Applications To Machine Learning Volume I Linear Algebra For Computer Vision Robotics And Machine Learning

Linear Algebra and Optimization with Applications to Machine Learning   Volume I  Linear Algebra for Computer Vision  Robotics  and Machine Learning PDF
Author: Jean H. Gallier
Publisher: World Scientific Publishing Company
ISBN: 9789811206399
Size: 45.94 MB
Format: PDF, ePub, Mobi
Category : Algebras, Linear
Languages : en
Pages : 824
View: 6945

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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, release date 2020, Linear Algebra And Optimization With Applications To Machine Learning Volume I Linear Algebra For Computer Vision Robotics And Machine Learning Books available in PDF, EPUB, Mobi Format. Download Linear Algebra And Optimization With Applications To Machine Learning Volume I Linear Algebra For Computer Vision Robotics And Machine Learning books, 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.




[PDF] Linear Algebra And Optimization With Applications To Machine Learning

Linear Algebra and Optimization with Applications to Machine Learning PDF
Author: Jean Gallier
Publisher: World Scientific Publishing Company
ISBN: 9789811216565
Size: 24.81 MB
Format: PDF
Category : Mathematics
Languages : en
Pages : 895
View: 2518

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Linear Algebra And Optimization With Applications To Machine Learning

by Jean Gallier, release date 2020-03-06, Linear Algebra And Optimization With Applications To Machine Learning Books available in PDF, EPUB, Mobi Format. Download Linear Algebra And Optimization With Applications To Machine Learning books, Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.




[PDF] Linear Algebra And Optimization With Applications To Machine Learning Volume I Linear Algebra For Computer Vision Robotics And Machine Learning

Linear Algebra And Optimization With Applications To Machine Learning   Volume I  Linear Algebra For Computer Vision  Robotics  And Machine Learning PDF
Author: Gallier Jean H
Publisher: World Scientific
ISBN: 9811206414
Size: 10.28 MB
Format: PDF
Category : Mathematics
Languages : en
Pages : 824
View: 6292

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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 Gallier Jean H, release date 2020-01-22, Linear Algebra And Optimization With Applications To Machine Learning Volume I Linear Algebra For Computer Vision Robotics And Machine Learning Books available in PDF, EPUB, Mobi Format. Download Linear Algebra And Optimization With Applications To Machine Learning Volume I Linear Algebra For Computer Vision Robotics And Machine Learning books, 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.




[PDF] Homology Cohomology And Sheaf Cohomology For Algebraic Topology Algebraic Geometry And Differential Geometry

Homology  Cohomology  And Sheaf Cohomology For Algebraic Topology  Algebraic Geometry  And Differential Geometry PDF
Author: Jean H Gallier
Publisher: World Scientific
ISBN: 9811245045
Size: 49.58 MB
Format: PDF
Category : Mathematics
Languages : en
Pages : 800
View: 6966

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Homology Cohomology And Sheaf Cohomology For Algebraic Topology Algebraic Geometry And Differential Geometry

by Jean H Gallier, release date 2022-01-19, Homology Cohomology And Sheaf Cohomology For Algebraic Topology Algebraic Geometry And Differential Geometry Books available in PDF, EPUB, Mobi Format. Download Homology Cohomology And Sheaf Cohomology For Algebraic Topology Algebraic Geometry And Differential Geometry books, For more than thirty years the senior author has been trying to learn algebraic geometry. In the process he discovered that many of the classic textbooks in algebraic geometry require substantial knowledge of cohomology, homological algebra, and sheaf theory. In an attempt to demystify these abstract concepts and facilitate understanding for a new generation of mathematicians, he along with co-author wrote this book for an audience who is familiar with basic concepts of linear and abstract algebra, but who never has had any exposure to the algebraic geometry or homological algebra. As such this book consists of two parts. The first part gives a crash-course on the homological and cohomological aspects of algebraic topology, with a bias in favor of cohomology. The second part is devoted to presheaves, sheaves, Cech cohomology, derived functors, sheaf cohomology, and spectral sequences. All important concepts are intuitively motivated and the associated proofs of the quintessential theorems are presented in detail rarely found in the standard texts.




[PDF] Linear Algebra And Optimization With Applications To Machine Learning Volume Ii Fundamentals Of Optimization Theory With Applications To Machine Learning

Linear Algebra And Optimization With Applications To Machine Learning   Volume Ii  Fundamentals Of Optimization Theory With Applications To Machine Learning PDF
Author: Quaintance Jocelyn
Publisher: World Scientific
ISBN: 9811216584
Size: 39.92 MB
Format: PDF
Category : Mathematics
Languages : en
Pages : 896
View: 2654

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Linear Algebra And Optimization With Applications To Machine Learning Volume Ii Fundamentals Of Optimization Theory With Applications To Machine Learning

by Quaintance Jocelyn, release date 2020-03-16, Linear Algebra And Optimization With Applications To Machine Learning Volume Ii Fundamentals Of Optimization Theory With Applications To Machine Learning Books available in PDF, EPUB, Mobi Format. Download Linear Algebra And Optimization With Applications To Machine Learning Volume Ii Fundamentals Of Optimization Theory With Applications To Machine Learning books, Volume 2 applies the linear algebra concepts presented in Volume 1 to optimization problems which frequently occur throughout machine learning. This book blends theory with practice by not only carefully discussing the mathematical under pinnings of each optimization technique but by applying these techniques to linear programming, support vector machines (SVM), principal component analysis (PCA), and ridge regression. Volume 2 begins by discussing preliminary concepts of optimization theory such as metric spaces, derivatives, and the Lagrange multiplier technique for finding extrema of real valued functions. The focus then shifts to the special case of optimizing a linear function over a region determined by affine constraints, namely linear programming. Highlights include careful derivations and applications of the simplex algorithm, the dual-simplex algorithm, and the primal-dual algorithm. The theoretical heart of this book is the mathematically rigorous presentation of various nonlinear optimization methods, including but not limited to gradient decent, the Karush-Kuhn-Tucker (KKT) conditions, Lagrangian duality, alternating direction method of multipliers (ADMM), and the kernel method. These methods are carefully applied to hard margin SVM, soft margin SVM, kernel PCA, ridge regression, lasso regression, and elastic-net regression. Matlab programs implementing these methods are included.




[PDF] Mathematics For Machine Learning

Mathematics for Machine Learning PDF
Author: Marc Peter Deisenroth
Publisher: Cambridge University Press
ISBN: 1108569323
Size: 70.34 MB
Format: PDF, ePub
Category : Computers
Languages : en
Pages :
View: 7242

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Mathematics For Machine Learning

by Marc Peter Deisenroth, release date 2020-04-23, Mathematics For Machine Learning Books available in PDF, EPUB, Mobi Format. Download Mathematics For Machine Learning books, The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.




[PDF] Linear Algebra And Optimization For Machine Learning

Linear Algebra and Optimization for Machine Learning PDF
Author: Charu C. Aggarwal
Publisher: Springer Nature
ISBN: 3030403440
Size: 47.32 MB
Format: PDF
Category : Computers
Languages : en
Pages : 495
View: 5394

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Linear Algebra And Optimization For Machine Learning

by Charu C. Aggarwal, release date 2020-05-13, Linear Algebra And Optimization For Machine Learning Books available in PDF, EPUB, Mobi Format. Download Linear Algebra And Optimization For Machine Learning books, This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout this text book together with access to a solution’s manual. 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.




[PDF] Efficient Processing Of Deep Neural Networks

Efficient Processing of Deep Neural Networks PDF
Author: Vivienne Sze
Publisher: Morgan & Claypool Publishers
ISBN: 1681738325
Size: 73.55 MB
Format: PDF, ePub, Mobi
Category : Computers
Languages : en
Pages : 341
View: 7565

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Efficient Processing Of Deep Neural Networks

by Vivienne Sze, release date 2020-06-24, Efficient Processing Of Deep Neural Networks Books available in PDF, EPUB, Mobi Format. Download Efficient Processing Of Deep Neural Networks books, This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of the DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as a formalization and organization of key concepts from contemporary works that provides insights that may spark new ideas.




[PDF] Linear Algebra For Dummies

Linear Algebra For Dummies PDF
Author: Mary Jane Sterling
Publisher: John Wiley & Sons
ISBN: 0470538163
Size: 41.65 MB
Format: PDF, ePub
Category : Mathematics
Languages : en
Pages : 384
View: 6987

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Linear Algebra For Dummies

by Mary Jane Sterling, release date 2009-06-05, Linear Algebra For Dummies Books available in PDF, EPUB, Mobi Format. Download Linear Algebra For Dummies books, 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