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Morgan Kaufmann

    • Advances in Computational Methods and Modeling for Science and Engineering

      • 1st Edition
      • February 4, 2025
      • Hari M Srivastava + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 3 0 0 1 2 7
      • eBook
        9 7 8 0 4 4 3 3 0 0 1 3 4
      Advances in Computational Methods and Modelling in Science and Engineering explores the application of computational techniques and modeling approaches in science and engineering, providing practical knowledge and skills for tackling complex problems using numerical simulations and data analysis. This book addresses the need for a cohesive and up-to-date resource in the rapidly evolving field of computational methods. It consolidates diverse topics, serving as a one-stop guide for individuals seeking a comprehensive understanding of the subject matter. Sections focus on mathematical techniques that provide global solutions for models arising in engineering and scientific research applications by considering their long-term benefits.The mathematical treatment of these models is very helpful in understanding these models and their real-world applications. The methods and modeling techniques presented are useful for mathematicians, engineers, scientists, and researchers working on the mathematical treatment of models in a wide range of applications, including disciplines such as engineering, physics, chemistry, computer science, and applied mathematics.

    • Data Mining

      • 5th Edition
      • February 4, 2025
      • Ian H. Witten + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 5 8 8 8 9
      • eBook
        9 7 8 0 4 4 3 1 5 8 8 9 6
      Data Mining: Practical Machine Learning Tools and Techniques, Fifth Edition, offers a thorough grounding in machine learning concepts, along with practical advice on applying these tools and techniques in real-world data mining situations. This highly anticipated new edition of the most acclaimed work on data mining and machine learning teaches readers everything they need to know to get going, from preparing inputs, interpreting outputs, evaluating results, to the algorithmic methods at the heart of successful data mining approaches.Extensive updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including more recent deep learning content on topics such as generative AI (GANs, VAEs, diffusion models), large language models (transformers, BERT and GPT models), and adversarial examples, as well as a comprehensive treatment of ethical and responsible artificial intelligence topics. Authors Ian H. Witten, Eibe Frank, Mark A. Hall, and Christopher J. Pal, along with new author James R. Foulds, include today’s techniques coupled with the methods at the leading edge of contemporary research

    • Applied Graph Data Science

      • 1st Edition
      • January 27, 2025
      • Pethuru Raj + 4 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 9 6 5 4 3
      • eBook
        9 7 8 0 4 4 3 2 9 6 5 5 0
      Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science. The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications. Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications. This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers. Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships. These data structures include graph query languages, databases, algorithms, and platforms. From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data. As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation. A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.

    • Probability for Deep Learning Quantum

      • 1st Edition
      • January 21, 2025
      • Charles R. Giardina
      • English
      • Paperback
        9 7 8 0 4 4 3 2 4 8 3 4 4
      • eBook
        9 7 8 0 4 4 3 2 4 8 3 5 1
      Probability for Deep Learning Quantum provides readers with the first book to address probabilistic methods in the deep learning environment and the quantum technological area simultaneously, by using a common platform: the Many-Sorted Algebra (MSA) view. While machine learning is created with a foundation of probability, probability is at the heart of quantum physics as well. It is the cornerstone in quantum applications. These applications include quantum measuring, quantum information theory, quantum communication theory, quantum sensing, quantum signal processing, quantum computing, quantum cryptography, and quantum machine learning. Although some of the probabilistic methods differ in machine learning disciplines from those in the quantum technologies, many techniques are very similar.Probability is introduced in the text rigorously, in Komogorov’s vision. It is however, slightly modified by developing the theory in a Many-Sorted Algebra setting. This algebraic construct is also used in showing the shared structures underlying much of both machine learning and quantum theory. Both deep learning and quantum technologies have several probabilistic and stochastic methods in common. These methods are described and illustrated using numerous examples within the text. Concepts in entropy are provided from a Shannon as well as a von-Neumann view. Singular value decomposition is applied in machine learning as a basic tool and presented in the Schmidt decomposition. Besides the in-common methods, Born’s rule as well as positive operator valued measures are described and illustrated, along with quasi-probabilities. Author Charles R. Giardina provides clear and concise explanations, accompanied by insightful and thought-provoking visualizations, to deepen your understanding and enable you to apply the concepts to real-world scenarios.

    • Agent-Based Models with MATLAB

      • 1st Edition
      • January 20, 2025
      • Erik Cuevas + 3 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 4 0 0 4 1
      • eBook
        9 7 8 0 4 4 3 2 4 0 0 5 8
      Agent-Based Models with MATLAB introduces Agent-Based Modeling (ABM), one of the most important methodologies for complex systems modeling. The book explores computational implementations and accompanying MATLAB software code as a means of inspiring readers to apply agent-based models to solve a diverse range of problems. It comes with a large amount of software code that accompanies the main text, and the modeling systems described in the book are implemented using MATLAB as the programming language. Despite the heavy mathematical components of Agent-Based Models and complex systems, it is possible to utilize these models without in-depth understanding of their mathematical fundamentals.This book enables computer scientists, mathematicians, researchers, and engineers to apply ABM in a wide range of research and engineering applications. It gradually advances from basic to more advanced methods while reinforcing complex systems through practical, hands-on applications of various computational models.

    • Neural Network Algorithms and Their Engineering Applications

      • 1st Edition
      • January 9, 2025
      • Chao Huang + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 2 9 2 0 2 6
      • eBook
        9 7 8 0 4 4 3 2 9 2 0 3 3
      Neural Network Algorithms and Their Engineering Applications presents the relevant techniques used to improve the global search ability of neural network algorithms in solving complex engineering problems with multimodal properties. The book provides readers with a complete study of how to use artificial neural networks to design a population-based metaheuristic algorithm, which in turn promotes the application of artificial neural networks in the field of engineering optimization.The authors provide a deep discussion for the potential application of machine learning methods in improving the optimization performance of the neural network algorithm, helping readers understand how to use machine learning methods to design improved versions of the algorithm. Users will find a wealth of source code that covers all applied algorithms. Code applications enhance readers' understanding of methods covered and facilitate readers' ability to apply the algorithms to their own research and development projects.

    • Programming Language Pragmatics

      • 5th Edition
      • January 9, 2025
      • Michael Scott + 1 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 9 9 6 6 3
      • eBook
        9 7 8 0 3 2 3 9 8 4 2 3 2
      Programming Language Pragmatics is the most comprehensive programming language textbook available today, with nearly 1000 pages of content in the book, plus hundreds more pages of reference materials and ancillaries online. Michael Scott takes theperspective that language design and language implementation are tightly interconnected, and that neither can be fully understood in isolation. In an approachable, readable style, he discusses more than 50 languages in the context of understanding how code isinterpreted or compiled, providing an organizational framework for learning new languages, regardless of platform. This edition has been thoroughly updated to cover the most recent developments in programming language design and provides both a solid understanding of the most important issues driving software development today

    • RISC-V System-On-Chip Design

      • 1st Edition
      • January 1, 2025
      • David Harris + 3 more
      • English
      • Paperback
        9 7 8 0 3 2 3 9 9 4 9 8 9
      • eBook
        9 7 8 0 3 2 3 9 9 4 9 9 6
      RISC-V Microprocessor System-On-Chip Design is written to be accessible to an advanced undergraduate audience with limited background. It explains concepts from operating systems, VLSI, and memory systems as necessary, and High school mathematics is sufficient preparation for most of the book, although the floating point and division chapters will be primarily of interest to those with a curiosity about computer arithmetic. Like Harris and Harris’s Digital Design and Computer Architecture textbooks, this book will appeal to students with easy-to-read and complete explanations, sidebars, and occasional humor and cartoons.It comes with an open-source implementation and will include end-of-chapter problems to extend the RISC-V processor in various ways. Ancillary materials include a GitHub repository with complete open-source SystemVerilog code, validation code in C and assembly language, and code for benchmarking and booting Linux.

    • Computer Architecture

      • 7th Edition
      • January 1, 2025
      • John L. Hennessy + 2 more
      • English
      • Paperback
        9 7 8 0 4 4 3 1 5 4 0 6 5
      • eBook
        9 7 8 0 4 4 3 1 5 4 0 7 2
      Computer Architecture: A Quantitative Approach, has been considered essential reading by instructors, students and practitioners of computer design for nearly 30 years. The seventh edition of this classic textbook from John Hennessy and David Patterson, winners of the 2017 ACM A.M. Turing Award recognizing contributions of lasting and major technical importance to the computing field, along with new author Christos Kozyrakis, is fully revised with the latest developments in processor and system architecture.True to its original mission of demystifying computer architecture, this edition continues the longstanding tradition of focusing on areas where the most exciting computing innovation is happening, while always keeping an emphasis on good engineering design.

    • Fixed Point Optimization Algorithms and Their Applications

      • 1st Edition
      • November 23, 2024
      • Watcharaporn Cholamjiak
      • English
      • Paperback
        9 7 8 0 4 4 3 3 3 5 8 6 0
      • eBook
        9 7 8 0 4 4 3 3 3 5 8 7 7
      Fixed Point Optimization Algorithms and Their Applications discusses how the relationship between fixed point algorithms and optimization problems is connected and demonstrates hands-on applications of the algorithms in fields such as image restoration, signal recovery, and machine learning. The book is divided into nine chapters beginning with foundational concepts of normed linear spaces, Banach spaces, and Hilbert spaces, along with nonlinear operators and useful lemmas and theorems for proving the book’s main results. The author presents algorithms for nonexpansive and generalized nonexpansive mappings in Hilbert space, and presents solutions to many optimization problems across a range of scientific research and real-world applications. From foundational concepts, the book proceeds to present a variety of optimization algorithms, including fixed point theories, convergence theorems, variational inequality problems, minimization problems, split feasibility problems, variational inclusion problems, and equilibrium problems. Fixed Point Optimization Algorithms and Their Applications equips readers with the theoretical mathematics background and necessary tools to tackle challenging optimization problems involving a range of algebraic methods, empowering them to apply these techniques in their research, professional work, or academic pursuits.

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