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Lavrač N: Bisociative Literature-based
Discovery

Bisociative Literature-based Discovery

Methods with Tutorials in Python

The SAS ecosystem is evolving. Whether you work with SAS Viya or SAS 9, there's never been a better time to dive in and master its full potential. This bookguides you through the technologies and environments that make SAS a powerful asset in data analytics, revealing the surprising depth and rich capabilities that SAS offers.This comprehensive guide is tailored for consultants and operations teams, covering everything from strategic analysis and architecture to implementation, administration, and best practices. You'll explore the analytics landscape across both commercial and open-source systems, learning how to design, deploy, and manage SAS environments effectively.By the end of this book, you'll be equipped to confidently implement and operate SAS technology turning raw data into real insights and business value.You will:·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Understand Consulting, Operations and Process activities associated with SAS technologies·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Support SAS data sourcing and reporting deliveries·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Establish best practices and guidelines for usage of the SAS platform&nbsp;&nbsp;

The SAS ecosystem is evolving. Whether you work with SAS Viya or SAS 9, there's never been a better time to dive in and master its full potential. This bookguides you through the technologies and environments that make SAS a powerful asset in data analytics, revealing the surprising depth and rich capabilities that SAS offers.This comprehensive guide is tailored for consultants and operations teams, covering everything from strategic analysis and architecture to implementation, administration, and best practices. You'll explore the analytics landscape across both commercial and open-source systems, learning how to design, deploy, and manage SAS environments effectively.By the end of this book, you'll be equipped to confidently implement and operate SAS technology turning raw data into real insights and business value.What You Will Learn·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Understand Consulting, Operations and Process activities associated with SAS technologies·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Support SAS data sourcing and reporting deliveries·&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Establish best practices and guidelines for usage of the SAS platformWho This Book Is ForConsultants, Administrators, Architects, and Developers&nbsp;

Mastering SAS Consulting: Design, Deploy, and Manage Analytics Environments

A Practitioners' Guide to SAS

Expert reference on building surrogate models, optimization using them, prediction uncertainty associate with them, and their potential failure, with practical implementation in MATLAB Surrogate Modeling and Optimization explains the meaning of different surrogate models and provides an in-depth understanding of such surrogates, emphasizing how much uncertainty is associated with them, and when and how a surrogate model can fail in approximating complex functions and helping readers understand theory through practical implementation in MATLAB. This book enables readers to obtain an accurate approximate function using as few samples as possible, thereby allowing them to replace expensive computer simulations and experiments during design optimization, sensitivity analysis, and/or uncertainty quantification. The book is organized into three parts. Part I introduces the basics of surrogate modeling. Part II reviews various theories and algorithms of design optimization. Part III presents advanced topics in surrogate modeling, including the Kriging surrogate, neural network models, multi-fidelity surrogates, and efficient global optimization using Kriging surrogates. The book is divided into 10 chapters. Each chapter contains about 10 examples and 20 exercise problems. Lecture slides and a solution manual for exercise problems are available for instructors on a companion website. Sample topics discussed in Surrogate Modeling and Optimization include: <ul> <li>Various designs of experiments, such as those developed for linear and quadratic polynomial response surfaces (PRS) in a boxlike design space</li> <li>Criteria for constrained and unconstrained optimization and the most important optimization theories</li> <li>Various numerical algorithms for gradient-based optimization</li> <li>Gradient-free optimization algorithms, often referred to as global search algorithms, which do not require gradient or Hessian information</li> <li>Detailed explanations and implementation on Kriging surrogate, often referred to as Gaussian Process, especially when samples include noise</li> <li>The combination of a small number of high-fidelity samples with many low-fidelity samples to improve prediction accuracy</li> <li>Neural network models, focusing on training uncertainty and its effect on prediction uncertainty</li> <li>Efficient global optimization using either polynomial response surfaces or Kriging surrofates</li> </ul> Surrogate Modeling and Optimization is an essential learning companion for senior-level undergraduate and graduate students in all engineering disciplines, including mechanical, aerospace, civil, biomedical, and electrical engineering. The book is also valuable for industrial practitioners who apply the surrogate model to solve their optimization problems.

Theories, Applications, and Limitations

Surrogate Modeling and Optimization

The Deep Learning Revolution

From the coauthor of Algorithms to Live By, an exploration of the quest to use mathematics to describe the ways we think, from its origins three hundred years ago to the ideas behind modern AI systems and the ways in which they still differ from human minds.Everyone has a basic understanding of how the physical world works. We learn about physics and chemistry in school, letting us explain the world around us in terms of concepts like force, acceleration, and gravity-the Laws of Nature. But we don't have the same fluency with concepts needed to understand the world inside us-the Laws of Thought. While the story of how mathematics has been used to reveal the mysteries of the universe is familiar, the story of how it has been used to study the mind is not.There is no one better to tell that story than Tom Griffiths, the head of Princeton's AI Lab and a renowned expert in the field of cognitive science. In this groundbreaking book, he explains the three major approaches to formalizing thought-rules and symbols, neural networks, and probability and statistics-introducing each idea through the stories of the people behind it. As informed conversations about thought, language, and learning become ever more pressing in the age of AI, The Laws of Thought is an essential read for anyone interested in the future of technology.

The Quest for a Mathematical Theory of the Mind

The Laws of Thought

Series On Intelligence Science

This popular science compendium provides an approximate, holistic but first ever solution to the four Holy Grail questions: How does a brain work? How does the brain learn? How does its consciousness arise? How does learning require consciousness?The volume explains how human brains require and learn consciousness and why the new AI will overcome the current lack of conscious learning algorithms in AI. For human societies, it suggests how governments can make their taxpayers safer, more prosper and happier. For future AI, it calls for a thorough investigation in scientific infrastructures like government and private funding agencies, publication venues, professional societies and administrators that evaluate research. Why did a conscious learning algorithm not have a healthy environment to study? Why could rampant misconducts of data deletion in deep learning be allowed to grossly exaggerate AI performances for so long? The author's real-life accounts reveal deep reasons.The useful reference text benefits laymen in all walks of life, as well as professionals, researchers, academics and students in any areas.

Conscious Learning: Humans And Machines

Specialty Crops and Climate Change Modeling

Artificial Intelligence and Data Science in Agriculture

Troubleshoot and Optimize Query Performance

SQL Server 2025 Query Performance Tuning

A Practical Guide Using Python

Classical Machine Learning

Generative AI and Agentic AI together are revolutionizing the technology landscape, with profound and far-reaching impacts across industries. Organizations are increasingly adopting these technologies to drive innovation, enhance unstructured content management, and improve problem-solving capabilities. With Agentic AI, enterprises are moving towards the development of intelligent systems that can plan, reason, and act with autonomy. While early proof-of-concepts (POCs) demonstrated the potential of these technologies, the current shift is toward responsible and scalable production implementations that leverage both generative and agentic capabilities. This book begins by guiding you through the technological evolution of AI, from early machine learning to today’s large language models (LLMs) and agentic systems. It then explores a wide range of use cases across industries, highlighting how LLMs can support decision-making, and how Agentic AI enables dynamic, collaborative systems that act with autonomy and intent. This is followed by Design Patterns across the lifecycle of AI solution development, deployment and monitoring. Readers will then gain insights into the methodologies for developing and deploying Generative and Agentic AI solutions at an enterprise level. A featured implementation demonstrates how Agentic AI can be effectively put into action. The book also introduces essential concepts such as MLOps, LLMOps, and Responsible AI principles which are critical for transitioning the AI solutions from experimentation to production. These principles ensure that AI deployments are scalable, secure, ethical and compliant. The book concludes with key takeaways and best practices for developing, evaluating, deploying and scaling AI applications responsibly and effectively within enterprise settings. You Will:<ul><li>Understand key design patterns to develop, deploy and monitor a Generative AI solution effectively.</li><li>Learn how to develop and implement a production-ready Agentic AI use case.</li><li>Discover best practices for building scalable, secure, and enterprise-grade AI solutions.</li><li>Understand how to assess and mitigate risks using Responsible AI principles and LLMOps best practices.</li></ul>This book is for : Enterprise Software Engineers and Architects

A Practical Guide to Developing, Deploying, and Operationalizing AI-Driven Applications for Enterprise Use

Enterprise Guide for Implementing Generative AI and Agentic AI

Six Dimensions of Business Dark Data

Agentic Hyper-Personalized Dimensions

Springer Handbooks

Springer Handbook of Chem- and Bioinformatics

Studies in Rhythm Engineering

This book is a multidisciplinary exploration of the concept of rhythm in diverse fields that are closely connected with our life and felt experience, namely, spirituality, language and literature, ecology, socio-cultural contexts, music, visual arts, and well-being. Bringing together a collection of 24 essays by both eminent and young scholars which cover these diverse fields, its uniqueness lies in the way that each essay provides insight into the concept of rhythm and also links itself to other related disciplines. A book bridges the divide between humanities, social, and behavioral sciences, and it is a must read book for anyone who is interested either in the concepts of rhythm, cycle, harmony, and their ubiquitous presence in all fields of knowledge.

Rhythms of Existence: From Cosmos to Human Experience

Bisociative Literature-based Discovery

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