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Morgan Kaufmann

Decentralized Optimization in Networks
Algorithmic Efficiency and Privacy Preservation
1st book:metaData.edition
Qingguo Lü contributors.plusContributors
publicationLanguages:en
Decentralized Optimization in Networks: Algorithmic Efficiency and Privacy Preservation provides the reader with theoretical foundations, practical guidance, and solutions to decentralized optimization problems. The book demonstrates the application of decentralized optimization algorithms to enhance communication and computational efficiency, solve large-scale datasets, maintain privacy preservation, and address challenges in complex decentralized networks. The book covers key topics such as event-triggered communication, random link failures, zeroth-order gradients, variance-reduction, Polyak’s projection, stochastic gradient, random sleep, and differential privacy. It also includes simulations and practical examples to illustrate the algorithms' effectiveness and applicability in real-world scenarios.
Mastering Prompt Engineering
Deep Insights for Optimizing Large Language Models (LLMs)
1st book:metaData.edition
Anand Nayyar contributors.plusContributors
publicationLanguages:en
Mastering Prompt Engineering: Deep Insights for Optimizing Large Language Models (LLMs) is a comprehensive guide that takes readers on a journey through the world of Large Language Models (LLMs) and prompt engineering. Covering foundational concepts, advanced techniques, ethical considerations, and real-world case studies, this book equips both novices and experts to navigate the complex LLM landscape. It provides insights into LLM architecture, training, and prompt engineering methods, while addressing ethical concerns such as bias and privacy. Real-world case studies showcase the practical application of prompt engineering in a wide range of settings. This resource is not just for specialists but is a practical and ethically conscious guide for AI practitioners, students, scientific researchers, and anyone interested in harnessing the potential of LLMs in natural language processing and generation. Mastering Prompt Engineering serves as a gateway to a deeper understanding of LLMs and their responsible and effective utilization through its comprehensive, ethical, and practical approach.
Decision Systems
Integrating Machine Learning, Fuzzy Logic, and Artificial Neural Networks
1st book:metaData.edition
Pallavi Vijay Chavan contributors.plusContributors
publicationLanguages:en
Decision-making is a fundamental process that influences outcomes across a wide range of domains, including business, healthcare, scientific research, and automation. With the increasing availability of data and the growing computational power of modern systems, decision-making models have become more sophisticated and capable of providing highly accurate and efficient solutions. The ability to develop, analyze, and implement these models has become crucial for professionals and researchers working in fields that rely on data-driven decision-making.This book explores the evolution and significance of decision systems, covering both foundational theories and advanced methodologies. It introduces readers to the essential principles of decision-making models, illustrating their applications through practical case studies and real-world scenarios. The discussion begins with a focus on traditional decision-making techniques and gradually progresses to more advanced topics, including machine learning-based approaches, the integration of artificial intelligence, and the role of fuzzy logic in decision support systems. Furthermore, ethical considerations in decision-making and strategies for mitigating bias are examined, ensuring that models remain fair and transparent.Througho... this book, each chapter builds on the previous one, providing a structured and comprehensive learning experience. By the time readers complete this book, they will have gained an in-depth understanding of decision-making frameworks, their applications, and the future directions of research in this dynamic field. Whether one is a student, a researcher, or an industry professional, this book serves as a valuable guide to mastering the complexities of decision systems and applying them effectively in various domains.
Quantum Computing
Principles and Paradigms
1st book:metaData.edition
Rajkumar Buyya contributors.plusContributors
publicationLanguages:en
Quantum Computing: Principles and Paradigms covers a broad range of topics, providing a state-of-the-art and comprehensive reference for the rapid progress in the field of quantum computing and related technologies from major international companies (such as IBM, Google, Intel, Rigetti, Q-Control) and academic researchers. This book appeals to a broad readership, as it covers comprehensive topics in the field of quantum computing, including hardware, software, algorithms, and applications, with chapters written by both academic researchers and industry developers.This book presents readers with the fundamental concepts of quantum computing research, along with the challenges involved in developing practical devices and applications.
Python Fast Track
A Complete Guide to Rapidly Mastering and Applying Python Programming
1st book:metaData.edition
Sanjiban Sekhar Roy contributors.plusContributors
publicationLanguages:en
Python Fast Track: A Complete Guide to Rapidly Mastering and Applying Python Programming adopts a simplified writing style and provides clear explanations to ensure ease of understanding, making it an ideal resource for those new to Python. Starting with the basics, the book covers fundamental concepts such as variables, data types, printing and prompting, lists, dictionaries, tuples, control structure, functions, and object-oriented concepts. The book includes everything you need to understand and apply more advanced programming techniques such as file handling, exception handling, and regex.This great resource is created especially for those who want to apply Python for their research and professional work in scientific computing, data analysis and machine learning, including chapters on NumPy and Pandas, two of the most popular Python application libraries. It demonstrates how to effectively master key applications of Python such as web development, software creation, task automation, and data analysis. The book covers data analysis and machine learning tasks that greatly benefit from Python, thanks to libraries such as TensorFlow and Keras that enable efficient coding.
The UX Book
Agile UX Design for a Quality User Experience
3rd book:metaData.edition
Rex Hartson contributors.plusContributors
publicationLanguages:en
The UX Book: Agile Design for a Quality User Experience, Third Edition, takes a practical, applied, hands-on approach to UX design based on the application of established and emerging best practices, principles, and proven methods to ensure a quality user experience. The approach is about practice, drawing on the creative concepts of design exploration and visioning to make designs that appeal to the emotions of users, while moving toward processes that are lightweight, rapid, and agile—to make things as good as resources permit and to value time and other resources in the process.Designed as a textbook for aspiring students and a how-to handbook and field guide for UX professionals, the book is accompanied by in-class exercises and team projects.The approach is practical rather than formal or theoretical. The primary goal is to imbue an understanding of what a good user experience is and how to achieve it. To better serve this, processes, methods, and techniques are introduced early to establish process-related concepts as context for discussion in later chapters.
Quantum Process Algebra
1st book:metaData.edition
Yong Wang
publicationLanguages:en
Quantum Process Algebra introduces readers to the algebraic properties and laws for quantum computing. The book provides readers with all aspects of algebraic theory for quantum computing, including the basis of semantics and axiomatization for quantum computing. With the assumption of a quantum system, readers will learn to solve the modeling of the three main components in a quantum system: the unitary operator, quantum measurement, and quantum entanglement, with full support of quantum and classical computing in closed systems. Next, the book establishes the relationship between probabilistic quantum bisimilarity and classical probabilistic bisimilarity, including strong probabilistic bisimilarity and weak probabilistic bisimilarity, which makes an axiomatization of quantum processes possible. With this framework, quantum and classical computing mixed processes are unified with the same structured operational semantics. Finally, the book establishes a series of axiomatizations of quantum process algebras. These process algebras support nearly all the main computation properties. Quantum and classical computing in closed quantum systems are unified with the same equational logic and the same structured operational semantics under the framework of ACP-like probabilistic process algebra. This unification means that the mathematics in the book can be used widely for verification of quantum and classical computing mixed systems, for example, most quantum communication protocols. ACP-like axiomatization also inherits the advantages of ACP, for example, and modularity means that it can be extended in an elegant way.
Artificial Neural Networks and Type-2 Fuzzy Set
Elements of Soft Computing and Its Applications
1st book:metaData.edition
Snehashish Chakraverty contributors.plusContributors
publicationLanguages:en
Soft computing is an emerging discipline which aims to exploit tolerance for imprecision, approximate reasoning, and uncertainty to achieve robustness, tractability, and cost effectiveness for building intelligent machines. Soft computing methodologies include neural networks, fuzzy sets, genetic algorithms, Bayesian networks, and rough sets, among others. In this regard, neural networks are widely used for modeling dynamic solvers, classification of data, and prediction of solutions, whereas fuzzy sets provide a natural framework for dealing with uncertainty. Artificial Neural Networks and Type-2 Fuzzy Set: Elements of Soft Computing and Its Applications covers the fundamental concepts and the latest research on variants of Artificial Neural Networks (ANN), including scientific machine learning and Type-2 Fuzzy Set (T2FS). In addition, the book also covers different applications for solving real-world problems along with various examples and case studies. It may be noted that quite a bit of research has been done on ANN and Fuzzy Set theory/ Fuzzy logic. However, Artificial Neural Networks and Type-2 Fuzzy Set is the first book to cover the use of ANN and fuzzy set theory with regards to Type-2 Fuzzy Set in static and dynamic problems in one place. Artificial Neural Networks and Type-2 Fuzzy Sets are two of the most widely used computational intelligence techniques for solving complex problems in various domains. Both ANN and T2FS have unique characteristics that make them suitable for different types of problems. This book provides the reader with in-depth understanding of how to apply these computational intelligence techniques in various fields of science and engineering in general and static and dynamic problems in particular. Further, for validation purposes of the ANN and fuzzy models, the obtained solutions of each model in the book is compared with already existing solutions that have been obtained with numerical or analytical methods.
Data Mining
Practical Machine Learning Tools and Techniques
5th book:metaData.edition
Ian H. Witten contributors.plusContributors
publicationLanguages:en
**2026 Textbook and Academic Authors Association (TAA) Textbook Excellence "Texty" Award Winner**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
Dimensionality Reduction in Machine Learning
1st book:metaData.edition
Jamal Amani Rad contributors.plusContributors
publicationLanguages:en
Dimensionality Reduction in Machine Learning covers both the mathematical and programming sides of dimension reduction algorithms, comparing them in various aspects. Part One provides an introduction to Machine Learning and the Data Life Cycle, with chapters covering the basic concepts of Machine Learning, essential mathematics for Machine Learning, and the methods and concepts of Feature Selection. Part Two covers Linear Methods for Dimension Reduction, with chapters on Principal Component Analysis and Linear Discriminant Analysis. Part Three covers Non-Linear Methods for Dimension Reduction, with chapters on Linear Local Embedding, Multi-dimensional Scaling, and t-distributed Stochastic Neighbor Embedding.Finally, Part Four covers Deep Learning Methods for Dimension Reduction, with chapters on Feature Extraction and Deep Learning, Autoencoders, and Dimensionality reduction in deep learning through group actions. With this stepwise structure and the applied code examples, readers become able to apply dimension reduction algorithms to different types of data, including tabular, text, and image data.

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