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Practical Linear Algebra: A Geometry Toolbox (Textbooks in Mathematics)
by Gerald Farin Dianne HansfordLinear algebra is growing in importance. 3D entertainment, animations in movies and video games are developed using linear algebra. Animated characters are generated using equations straight out of this book. Linear algebra is used to extract knowledge from the massive amounts of data generated from modern technology. The Fourth Edition of this popular text introduces linear algebra in a comprehensive, geometric, and algorithmic way. The authors start with the fundamentals in 2D and 3D, then move on to higher dimensions, expanding on the fundamentals and introducing new topics, which are necessary for many real-life applications and the development of abstract thought. Applications are introduced to motivate topics. The subtitle, A Geometry Toolbox, hints at the book’s geometric approach, which is supported by many sketches and figures. Furthermore, the book covers applications of triangles, polygons, conics, and curves. Examples demonstrate each topic in action. This practical approach to a linear algebra course, whether through classroom instruction or self-study, is unique to this book. New to the Fourth Edition: Ten new application sections. A new section on change of basis. This concept now appears in several places. Chapters 14-16 on higher dimensions are notably revised. A deeper look at polynomials in the gallery of spaces. Introduces the QR decomposition and its relevance to least squares. Similarity and diagonalization are given more attention, as are eigenfunctions. A longer thread on least squares, running from orthogonal projections to a solution via SVD and the pseudoinverse. More applications for PCA have been added. More examples, exercises, and more on the kernel and general linear spaces. A list of applications has been added in Appendix A. The book gives instructors the option of tailoring the course for the primary interests of their students: mathematics, engineering, science, computer graphics, and geometric modeling.
Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python
by Mike X CohenIf you want to work in any computational or technical field, you need to understand linear algebra. As the study of matrices and operations acting upon them, linear algebra is the mathematical basis of nearly all algorithms and analyses implemented in computers. But the way it's presented in decades-old textbooks is much different from how professionals use linear algebra today to solve real-world modern applications.This practical guide from Mike X Cohen teaches the core concepts of linear algebra as implemented in Python, including how they're used in data science, machine learning, deep learning, computational simulations, and biomedical data processing applications. Armed with knowledge from this book, you'll be able to understand, implement, and adapt myriad modern analysis methods and algorithms.Ideal for practitioners and students using computer technology and algorithms, this book introduces you to:The interpretations and applications of vectors and matricesMatrix arithmetic (various multiplications and transformations)Independence, rank, and inversesImportant decompositions used in applied linear algebra (including LU and QR)Eigendecomposition and singular value decompositionApplications including least-squares model fitting and principal components analysis
Practical Longitudinal Data Analysis (Chapman And Hall/crc Texts In Statistical Science Ser. #34)
by David J. HandThis text describes regression-based approaches to analyzing longitudinal and repeated measures data. It emphasizes statistical models, discusses the relationships between different approaches, and uses real data to illustrate practical applications. It uses commercially available software when it exists and illustrates the program code and output. The data appendix provides many real data sets-beyond those used for the examples-which can serve as the basis for exercises.
Practical Machine Learning Illustrated with KNIME
by Yu Geng Qin Li Geng Yang Wan QiuThis book guides professionals and students from various backgrounds to use machine learning in their own fields with low-code platform KNIME and without coding. Many people from various industries need use machine learning to solve problems in their own domains. However, machine learning is often viewed as the domain of programmers, especially for those who are familiar with Python. It is too hard for people from different backgrounds to learn Python to use machine learning. KNIME, the low-code platform, comes to help. KNIME helps people use machine learning in an intuitive environment, enabling everyone to focus on what to do instead of how to do. This book helps the readers gain an intuitive understanding of the basic concepts of machine learning through illustrations to practice machine learning in their respective fields. The author provides a practical guide on how to participate in Kaggle completions with KNIME to practice machine learning techniques.
Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI
by Darren CookMachine learning has finally come of age. With H2O software, you can perform machine learning and data analysis using a simple open source framework that’s easy to use, has a wide range of OS and language support, and scales for big data. This hands-on guide teaches you how to use H20 with only minimal math and theory behind the learning algorithms.If you’re familiar with R or Python, know a bit of statistics, and have some experience manipulating data, author Darren Cook will take you through H2O basics and help you conduct machine-learning experiments on different sample data sets. You’ll explore several modern machine-learning techniques such as deep learning, random forests, unsupervised learning, and ensemble learning.Learn how to import, manipulate, and export data with H2OExplore key machine-learning concepts, such as cross-validation and validation data setsWork with three diverse data sets, including a regression, a multinomial classification, and a binomial classificationUse H2O to analyze each sample data set with four supervised machine-learning algorithmsUnderstand how cluster analysis and other unsupervised machine-learning algorithms work
Practical Machine Learning with R: Tutorials and Case Studies
by Carsten LangeThis textbook is a comprehensive guide to machine learning and artificial intelligence tailored for students in business and economics. It takes a hands-on approach to teach machine learning, emphasizing practical applications over complex mathematical concepts. Students are not required to have advanced mathematics knowledge such as matrix algebra or calculus.The author introduces machine learning algorithms, utilizing the widely used R language for statistical analysis. Each chapter includes examples, case studies, and interactive tutorials to enhance understanding. No prior programming knowledge is needed. The book leverages the tidymodels package, an extension of R, to streamline data processing and model workflows. This package simplifies commands, making the logic of algorithms more accessible by minimizing programming syntax hurdles. The use of tidymodels ensures a unified experience across various machine learning models.With interactive tutorials that students can download and follow along at their own pace, the book provides a practical approach to apply machine learning algorithms to real-world scenarios.In addition to the interactive tutorials, each chapter includes a Digital Resources section, offering links to articles, videos, data, and sample R code scripts. A companion website further enriches the learning and teaching experience: https://ai.lange-analytics.com.This book is not just a textbook; it is a dynamic learning experience that empowers students and instructors alike with a practical and accessible approach to machine learning in business and economics. Key Features: Unlocks machine learning basics without advanced mathematics — no calculus or matrix algebra required. Demonstrates each concept with R code and real-world data for a deep understanding — no prior programming knowledge is needed. Bridges the gap between theory and real-world applications with hands-on interactive projects and tutorials in every chapter, guided with hints and solutions. Encourages continuous learning with chapter-specific online resources—video tutorials, R-scripts, blog posts, and an online community. Supports instructors through a companion website that includes customizable materials such as slides and syllabi to fit their specific course needs.
Practical Math Applications: Practical Math Applications, 3rd + Cengagenow Printed Access Card
by Sharon Burton Nelda SheltonNIMAC-sourced textbook
Practical Mathematical Cryptography (Chapman & Hall/CRC Cryptography and Network Security Series)
by Kristian GjøsteenPractical Mathematical Cryptography provides a clear and accessible introduction to practical mathematical cryptography. Cryptography, both as a science and as practice, lies at the intersection of mathematics and the science of computation, and the presentation emphasises the essential mathematical nature of the computations and arguments involved in cryptography. Cryptography is also a practical science, and the book shows how modern cryptography solves important practical problems in the real world, developing the theory and practice of cryptography from the basics to secure messaging and voting. The presentation provides a unified and consistent treatment of the most important cryptographic topics, from the initial design and analysis of basic cryptographic schemes towards applications. Features Builds from theory toward practical applications Suitable as the main text for a mathematical cryptography course Focus on secure messaging and voting systems.
Practical Mathematical Optimization: An Introduction To Basic Optimization Theory And Classical And New Gradient-based Algorithms (Applied Optimization Ser. #97)
by Daniel N. Wilke Jan A. SnymanThis book presents basic optimization principles and gradient-based algorithms to a general audience, in a brief and easy-to-read form. It enables professionals to apply optimization theory to engineering, physics, chemistry, or business economics.
Practical Mathematics: Consumer Applications
by Marguerite M. Fredrick Robert D. Postman Steven J. Leinwand Laurence R. WantuckA practical mathematics textbook
Practical Mathematics for Consumers (The Pacemaker Curriculum: Careers)
by FearonThis straightforward, easy-to-understand program provides students of mixed abilities with key math concepts essential for successful adult living. From buying groceries to budgeting for housing, education, and travel, the simply stated subject matter delivered in a manageable format with a controlled reading level makes content accessible to all students. Students gain practical math competence through real-world examples in the areas of money management, banking, credit-card math, career choices, consumerism, jobs, and everyday living. Basic skills lessons review and practice mathematical concepts essential to everyday life.
Practical mathematics in a commercial metropolis
by Ad MeskensDescribes the development and the ultimate demise of the practice of mathematics in sixteenth century Antwerp. Against the background of the violent history of the Religious Wars the story of the practice of mathematics in Antwerp is told through the lives of two protagonists Michiel Coignet and Peeter Heyns. The book touches on all aspects of practical mathematics from teaching and instrument making to the practice of building fortifications of the practice of navigation.
Practical MATLAB: With Modeling, Simulation, and Processing Projects
by Irfan TurkApply MATLAB programming to the mathematical modeling of real-life problems from a wide range of topics. This pragmatic book shows you how to solve your programming problems, starting with a brief primer on MATLAB and the fundamentals of the MATLAB programming language. Then, you’ll build fully working examples and computational models found in the financial, engineering, and scientific sectors. As part of this section, you’ll cover signal and image processing, as well as GUIs. After reading and using Practical MATLAB and its accompanying source code, you’ll have the practical know-how and code to apply to your own MATLAB programming projects. What You Will LearnDiscover the fundamentals of MATLAB and how to get started with it for problem solvingApply MATLAB to a variety of problems and case studiesCarry out economic and financial modeling with MATLAB, including option pricing and compound interestUse MATLAB for simulation problems such as coin flips, dice rolling, random walks, and traffic flowsSolve computational biology problems with MATLABImplement signal processing with MATLAB, including currents, Fast Fourier Transforms (FFTs), and harmonic analysisProcess images with filters and edge detectionBuild applications with GUIs Who This Book Is ForPeople with some prior experience with programming and MATLAB.
Practical MATLAB Applications for Engineers (Practical Matlab for Engineers)
by Misza KalechmanPractical Matlab Applications for Engineers provides a tutorial for those with a basic understanding of Matlab®. It can be used to follow Misza Kalechman’s, Practical Matlab Basics for Engineers (cat no. 47744). This volume explores the concepts and Matlab tools used in the solution of advanced course work for engineering and technology students. It covers the material encountered in the typical engineering and technology programs at most colleges. It illustrates the direct connection between theory and real applications. Each chapter reviews basic concepts and then explores those concepts with a number of worked out examples.
Practical MATLAB Basics for Engineers (Practical Matlab for Engineers)
by Misza KalechmanA comprehensive and accessible primer, this tutorial immerses engineers and engineering students in the essential technical skills that will allow them to put Matlab® to immediate use. The book covers concepts such as: functions, algebra, geometry, arrays, vectors, matrices, trigonometry, graphs, pre-calculus and calculus. It then delves into the Matlab language, covering syntax rules, notation, operations, computational programming, and general problem solving in the areas of applied mathematics and general physics. This knowledge can be used to explore the basic applications that are detailed in Misza Kalechman’s companion volume, Practical Matlab Applications for Engineers (cat no. 47760). .
Practical MATLAB Modeling with Simulink: Programming and Simulating Ordinary and Partial Differential Equations
by Sulaymon L. EshkabilovEmploy the essential and hands-on tools and functions of MATLAB's ordinary differential equation (ODE) and partial differential equation (PDE) packages, which are explained and demonstrated via interactive examples and case studies. This book contains dozens of simulations and solved problems via m-files/scripts and Simulink models which help you to learn programming and modeling of more difficult, complex problems that involve the use of ODEs and PDEs.You’ll become efficient with many of the built-in tools and functions of MATLAB/Simulink while solving more complex engineering and scientific computing problems that require and use differential equations. Practical MATLAB Modeling with Simulink explains various practical issues of programming and modelling.After reading and using this book, you'll be proficient at using MATLAB and applying the source code from the book's examples as templates for your own projects in data science or engineering. What You Will LearnModel complex problems using MATLAB and SimulinkGain the programming and modeling essentials of MATLAB using ODEs and PDEsUse numerical methods to solve 1st and 2nd order ODEsSolve stiff, higher order, coupled, and implicit ODEsEmploy numerical methods to solve 1st and 2nd order linear PDEsSolve stiff, higher order, coupled, and implicit PDEsWho This Book Is ForEngineers, programmers, data scientists, and students majoring in engineering, applied/industrial math, data science, and scientific computing. This book continues where Apress' Beginning MATLAB and Simulink leaves off.
Practical Methods of Optimization
by R. FletcherFully describes optimization methods that are currently most valuable in solving real-life problems. Since optimization has applications in almost every branch of science and technology, the text emphasizes their practical aspects in conjunction with the heuristics useful in making them perform more reliably and efficiently. To this end, it presents comparative numerical studies to give readers a feel for possibile applications and to illustrate the problems in assessing evidence. Also provides theoretical background which provides insights into how methods are derived. This edition offers revised coverage of basic theory and standard techniques, with updated discussions of line search methods, Newton and quasi-Newton methods, and conjugate direction methods, as well as a comprehensive treatment of restricted step or trust region methods not commonly found in the literature. Also includes recent developments in hybrid methods for nonlinear least squares; an extended discussion of linear programming, with new methods for stable updating of LU factors; and a completely new section on network programming. Chapters include computer subroutines, worked examples, and study questions.
Practical Multilevel Modeling Using R (Advanced Quantitative Techniques in the Social Sciences)
by Francis L. HuangPractical Multilevel Modeling Using R provides students with a step-by-step guide for running their own multilevel analyses. Detailed examples illustrate the conceptual and statistical issues that multilevel modeling addresses in a way that is clear and relevant to students in applied disciplines. Clearly annotated R syntax illustrates how multilevel modeling (MLM) can be used, and real-world examples show why and how modeling decisions can affect results. The book covers all the basics but also important advanced topics such as diagnostics, detecting and handling heteroscedasticity, power analysis, and missing data handling methods. Unlike other detailed texts on MLM which are written at a very high level, this text with its applied focus and use of R software to run the analyses is much more suitable for students who have substantive research areas but are not training to be methodologists or statisticians. Each chapter concludes with a "Test Yourself" section, and solutions are available on the instructor website for the book. A companion R package is available for use with this text.
Practical Multilevel Modeling Using R (Advanced Quantitative Techniques in the Social Sciences)
by Francis L. HuangPractical Multilevel Modeling Using R provides students with a step-by-step guide for running their own multilevel analyses. Detailed examples illustrate the conceptual and statistical issues that multilevel modeling addresses in a way that is clear and relevant to students in applied disciplines. Clearly annotated R syntax illustrates how multilevel modeling (MLM) can be used, and real-world examples show why and how modeling decisions can affect results. The book covers all the basics but also important advanced topics such as diagnostics, detecting and handling heteroscedasticity, power analysis, and missing data handling methods. Unlike other detailed texts on MLM which are written at a very high level, this text with its applied focus and use of R software to run the analyses is much more suitable for students who have substantive research areas but are not training to be methodologists or statisticians. Each chapter concludes with a "Test Yourself" section, and solutions are available on the instructor website for the book. A companion R package is available for use with this text.
Practical Multivariate Analysis (Chapman & Hall/CRC Texts in Statistical Science)
by Abdelmonem Afifi Susanne May Robin Donatello Virginia A. ClarkThis is the sixth edition of a popular textbook on multivariate analysis. Well-regarded for its practical and accessible approach, with excellent examples and good guidance on computing, the book is particularly popular for teaching outside statistics, i.e. in epidemiology, social science, business, etc. The sixth edition has been updated with a new chapter on data visualization, a distinction made between exploratory and confirmatory analyses and a new section on generalized estimating equations and many new updates throughout. This new edition will enable the book to continue as one of the leading textbooks in the area, particularly for non-statisticians. Key Features: Provides a comprehensive, practical and accessible introduction to multivariate analysis. Keeps mathematical details to a minimum, so particularly geared toward a non-statistical audience. Includes lots of detailed worked examples, guidance on computing, and exercises. Updated with a new chapter on data visualization.
Practical Neo4j
by Gregory JordanWhy have developers at places like Facebook and Twitter increasingly turned to graph databases to manage their highly connected big data? The short answer is that graphs offer superior speed and flexibility to get the job done. It's time you added skills in graph databases to your toolkit. In Practical Neo4j, database expert Greg Jordan guides you through the background and basics of graph databases and gets you quickly up and running with Neo4j, the most prominent graph database on the market today. Jordan walks you through the data modeling stages for projects such as social networks, recommendation engines, and geo-based applications. The book also dives into the configuration steps as well as the language options used to create your Neo4j-backed applications. Neo4j runs some of the largest connected datasets in the world, and developing with it offers you a fast, proven NoSQL database option. Besides those working for social media, database, and networking companies of all sizes, academics and researchers will find Neo4j a powerful research tool that can help connect large sets of diverse data and provide insights that would otherwise remain hidden. Using Practical Neo4j, you will learn how to harness that power and create elegant solutions that address complex data problems. This book: Explains the basics of graph databasesDemonstrates how to configure and maintain Neo4jShows how to import data into Neo4j from a variety of sourcesProvides a working example of a Neo4j-based application using an array of language of options including Java, . Net, PHP, Python, Spring, and RubyAs you'll discover, Neo4j offers a blend of simplicity and speed while allowing data relationships to maintain first-class status. That's one reason among many that such a wide range of industries and fields have turned to graph databases to analyze deep, dense relationships. After reading this book, you'll have a potent, elegant tool you can use to develop projects profitably and improve your career options.
Practical Numerical and Scientific Computing with MATLAB® and Python
by Eihab B. BashierPractical Numerical and Scientific Computing with MATLAB® and Python concentrates on the practical aspects of numerical analysis and linear and non-linear programming. It discusses the methods for solving different types of mathematical problems using MATLAB and Python. Although the book focuses on the approximation problem rather than on error analysis of mathematical problems, it provides practical ways to calculate errors. The book is divided into three parts, covering topics in numerical linear algebra, methods of interpolation, numerical differentiation and integration, solutions of differential equations, linear and non-linear programming problems, and optimal control problems. This book has the following advantages: It adopts the programming languages, MATLAB and Python, which are widely used among academics, scientists, and engineers, for ease of use and contain many libraries covering many scientific and engineering fields. It contains topics that are rarely found in other numerical analysis books, such as ill-conditioned linear systems and methods of regularization to stabilize their solutions, nonstandard finite differences methods for solutions of ordinary differential equations, and the computations of the optimal controls. It provides a practical explanation of how to apply these topics using MATLAB and Python. It discusses software libraries to solve mathematical problems, such as software Gekko, pulp, and pyomo. These libraries use Python for solutions to differential equations and static and dynamic optimization problems. Most programs in the book can be applied in versions prior to MATLAB 2017b and Python 3.7.4 without the need to modify these programs. This book is aimed at newcomers and middle-level students, as well as members of the scientific community who are interested in solving math problems using MATLAB or Python.
Practical Numerical C Programming: Finance, Engineering, and Physics Applications
by Philip JoyceMaster the C code appropriate for numerical methods and computational modeling, including syntax, loops, subroutines, and files. Then, this hands-on book dives into financial applications using regression models, product moment correlation coefficients, and asset pricing. Next, Practical Numerical C Programming covers applications for engineering/business such as supermarket stock reordering simulation as well as flight information boards at airports and controlling a power plant. Finally, the book concludes with some physics including building simulation models for energy and pendulum motion. Along the way, you’ll learn center-of-mass calculations, Brownian motion, and more. After reading and using this book, you'll come away with pragmatic case studies of actual applications using C code at work. Source code is freely available and includes the latest C20 standard release. What You Will Learn Apply regression techniques to find the pattern for depreciation of the value of cars over a period of years Work with the product moment correlation coefficient technique to illustrate the accuracy (or otherwise) of regression techniques Use the past stock values of an asset to predict what its future values may be using Monte Carlo methods Simulate the buying of supermarket stock by shoppers and check the remaining stock: if it is too low print a message to reorder the stock Create a file of arrivals for an airport and send data to the airport’s display boards to show the current situation for the incoming flights Simulate the patterns of particles moving in gases or solids Who This Book Is For Programmers and computational modelers with at least some prior experience with programming in C as well as programming in general.
The Practical OPNET User Guide for Computer Network Simulation
by Adarshpal S. Sethi Vasil Y. HnatyshinOne of the first books to provide a comprehensive description of OPNET IT Guru and Modeler software, The Practical OPNET User Guide for Computer Network Simulation explains how to use this software for simulating and modeling computer networks. The included laboratory projects help readers learn different aspects of the software in a hands-on way.Q
Practical PowerShell Security and Compliance Center: Get to grips with effectively managing the Security and Compliance Center with PowerShell, 2nd Edition
by Damian ScolesLearn all the latest practices to build and enhance your PowerShell scripts and manage the Security and Compliance Center easily and efficiently Key Features Gather the latest information and new cmdlets to get started with PowerShell Discover innovative ways to construct useful scripts Learn how to manage SCC with PowerShell through real-world scenarios Book Description PowerShell is an integral part of Office 365. This updated edition shares tips, tricks, new cmdlets, and best practices for using PowerShell with the Security and Compliance Center. The book starts by covering the basic tools of PowerShell, such as PowerShell ISE and PowerShell repositories, and then goes on to teach you how to connect to the SCC using multi-factor authentication. You will manage role groups to layer security, before exploring the importance of information barriers. As you progress, you will orchestrate insider risk policies to protect your organization from malicious users with access to sensitive data. Next, the book will show you how to manage various devices using tenant policies and device access rules. Later chapters will delve into various reporting methods and explore options for troubleshooting scripts and best practices to produce better scripts. The book concludes with an overview of Microsoft Secure Score to help you measure your organization's security posture. By the end of this book, you'll have the latest information and new cmdlets to confidently manage the Security and Compliance Center (SCC) with PowerShell. What you will learn Enhance the script, test it, and move it into production Make your PowerShell scripts more usable and accessible Study directory synchronization and its applications Explore layers of security that prevent unauthorized access Use PowerShell to implement Data Loss Prevention (DLP) Understand compliance and how it can be managed with PowerShell Who this book is for If you are already managing Exchange and have some PowerShell experience, then this book is for you. Professionals who want to gain more proficiency in PowerShell but are unsure of how to approach it will also find this book useful. To get the most out of this book, you must have sufficient experience of working in PowerShell as the book is not a beginner's guide to PowerShell.