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Author: Katherine Franklin Publisher: Brooks/Cole ISBN: 9780534132842 Category : Mathematics Languages : en Pages : 714
Book Description
While maintaining its focus on functions and graphs this book gives the adequately prepared algebra student the right start and flexible goals.
Author: Katherine Franklin Publisher: Brooks/Cole ISBN: 9780534132842 Category : Mathematics Languages : en Pages : 714
Book Description
While maintaining its focus on functions and graphs this book gives the adequately prepared algebra student the right start and flexible goals.
Author: Jay Abramson Publisher: ISBN: 9789888407439 Category : Mathematics Languages : en Pages : 892
Book Description
College Algebra provides a comprehensive exploration of algebraic principles and meets scope and sequence requirements for a typical introductory algebra course. The modular approach and richness of content ensure that the book meets the needs of a variety of courses. College Algebra offers a wealth of examples with detailed, conceptual explanations, building a strong foundation in the material before asking students to apply what they've learned. Coverage and Scope In determining the concepts, skills, and topics to cover, we engaged dozens of highly experienced instructors with a range of student audiences. The resulting scope and sequence proceeds logically while allowing for a significant amount of flexibility in instruction. Chapters 1 and 2 provide both a review and foundation for study of Functions that begins in Chapter 3. The authors recognize that while some institutions may find this material a prerequisite, other institutions have told us that they have a cohort that need the prerequisite skills built into the course. Chapter 1: Prerequisites Chapter 2: Equations and Inequalities Chapters 3-6: The Algebraic Functions Chapter 3: Functions Chapter 4: Linear Functions Chapter 5: Polynomial and Rational Functions Chapter 6: Exponential and Logarithm Functions Chapters 7-9: Further Study in College Algebra Chapter 7: Systems of Equations and Inequalities Chapter 8: Analytic Geometry Chapter 9: Sequences, Probability and Counting Theory
Author: Vladimir Rovenski Publisher: Springer Science & Business Media ISBN: 0387712771 Category : Mathematics Languages : en Pages : 463
Book Description
This text on geometry is devoted to various central geometrical topics including: graphs of functions, transformations, (non-)Euclidean geometries, curves and surfaces as well as their applications in a variety of disciplines. This book presents elementary methods for analytical modeling and demonstrates the potential for symbolic computational tools to support the development of analytical solutions. The author systematically examines several powerful tools of MATLAB® including 2D and 3D animation of geometric images with shadows and colors and transformations using matrices. With over 150 stimulating exercises and problems, this text integrates traditional differential and non-Euclidean geometries with more current computer systems in a practical and user-friendly format. This text is an excellent classroom resource or self-study reference for undergraduate students in a variety of disciplines.
Author: Katherine Yoshiwara Publisher: Brooks Cole ISBN: 9780534436919 Category : Languages : en Pages : 600
Book Description
Popular with and respected by instructors and students interested in a modeling approach, graphing, or graphing calculators, this book incorporates the benefits of technology and the philosophy of the reform movement into intermediate algebra. In keeping with the NCTM and AMATYC standards, the authors introduce the techniques of algebra in the context of simple applications. Early and consistent emphasis on functions and graphing helps to develop mathematical models, and graphing calculators are incorporated wherever possible.
Author: Judith A. Beecher Publisher: Addison-Wesley Longman ISBN: 9780321693990 Category : Algebra Languages : en Pages : 0
Book Description
Beecher, Penna, and Bittinger's College Algebra is known for enabling students to "see the math" through its focus on visualization and early introduction to functions. With the Fourth Edition, the authors continue to innovate by incorporating more ongoing review to help students develop their understanding and study effectively. Mid-chapter Review exercise sets have been added to give students practice in synthesizing the concepts, and new Study Summaries provide built-in tools to help them prepare for tests. The MyMathLab course (access kit required) has been expanded so that the online content is even more integrated with the text's approach, with the addition of Vocabulary, Synthesis, and Mid-chapter Review exercises from the text as well as example-based videos created by the authors.
Author: William L. William L. Hamilton Publisher: Springer Nature ISBN: 3031015886 Category : Computers Languages : en Pages : 141
Book Description
Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.
Author: Wolfgang Borutzky Publisher: Springer Science & Business Media ISBN: 1441993681 Category : Technology & Engineering Languages : en Pages : 446
Book Description
The author presents current work in bond graph methodology by providing a compilation of contributions from experts across the world that covers theoretical topics, applications in various areas as well as software for bond graph modeling. It addresses readers in academia and in industry concerned with the analysis of multidisciplinary engineering systems or control system design who are interested to see how latest developments in bond graph methodology with regard to theory and applications can serve their needs in their engineering fields. This presentation of advanced work in bond graph modeling presents the leading edge of research in this field. It is hoped that it stimulates new ideas with regard to further progress in theory and in applications.
Author: Katherine Franklin
Author: Katherine Franklin
Author: Katherine Yoshiwara
Author: Jay Abramson
Author: Vladimir Rovenski
Author: Katherine Yoshiwara
Author: Judith A. Beecher
Author: William L. William L. Hamilton
Author: Wolfgang Borutzky
Author: Jack Burrill
Author: Charles P. McKeague