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Stanimirović P: Generalized Matrix
Inversion: A Machine Learning Approach

Generalized Matrix Inversion: A Machine Learning Approach

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

Concepts, Models, and Applications

Learning-Driven Game Theory for AI

Applications in R and Python

Essentials of Big Data Analytics

Methods, Applications, and Implications

Adaptive AI in Sensor Informatics

Advanced Intelligence Methods for Data Science and Optimization covers the latest research trends and applications of AI topics such as deep learning, reinforcement learning, evolutionary algorithms, Bayesian optimization, and swarm intelligence. The book is a comprehensive guide that provides readers with theoretical concepts and case studies for applying advanced intelligence methods to real-world problems. Authored by a team of renowned experts in the field, the book offers a holistic approach to understanding and applying intelligence methods across various domains. It explores the fundamental concepts of data science and optimization, providing a strong foundation for readers to build upon, and will be a welcomed resource for AI researchers, data scientists, engineers, and developers on key topics such as evolutionary optimization techniques, reinforcement learning, Natural Language Processing, Bayesian optimization, advanced analytics for large-scale data, fuzzy logic, quantum computing, graph theory, convex optimization, differential evolution, and more.

Advanced Intelligence Methods for Data Science and Optimization

This companion volume to Artificial Intelligence: A Guide for Everyone offers a comprehensive exploration of AI analytics, catering to individuals of all backgrounds and expertise levels. It seeks to demystify AI analytics by exploring core concepts, explaining various stages (Descriptive, Diagnostic, Predictive, and Prescriptive), and highlighting their limitations. Through domain-specific applications, it illustrates how AI is utilized across industries for innovation and efficiency, providing dedicated subsections for healthcare, finance, customer support, and more.Artificial Intelligence: Analytics, Platforms, and Risks thoroughly examines the AI infrastructure and ecosystem, offering insights into frameworks, platforms, tools, key players, and the critical hardware and software components supporting AI applications. It also addresses the multifaceted risks and challenges of AI, such as bad data, model selection bias, job displacement, weaponization, and ethical dilemmas, along with strategies to mitigate these risks.&nbsp;Additionally, it discusses the hurdles in AI adoption, including roadblocks, myths, planning complexities, resource requirements, and data and model challenges. Looking ahead, the book explores the future of AI, highlighting catalysts for technological progress, the transformative impact across industries, and emerging trends shaping the field. With the aim of empowering readers to navigate the complexities of AI, harness its potential, and contribute to its responsible and ethical advancement, this book serves as a comprehensive guide to AI technologies.

Artificial Intelligence: Analytics, Platforms, and Risks

This book is the first one that shows how to accurately and efficiently solve time-varying problems in real-time or online using continuous- or discrete-time zeroing dynamics. The authors provide a comprehensive treatment of the theory of both static and dynamic neural networks. They develop, analyze, model, simulate, and compare zeroing dynamics models for the online solution of numerous time-varying problems, such as root finding, nonlinear equation solving, matrix inversion, matrix square root finding, quadratic optimization, and inequality solving.

Zeroing Dynamics, Gradient Dynamics, and Newton Iterations

Key Technologies on New Energy Vehicles

This book focuses on the current advanced technology field of battery operation big data and comprehensively describes the frontier technology of big data analysis and intelligent control of battery safe operation. The content systematically introduces the coupling mechanism of battery system safety and consistency, battery SOH estimation and prediction, battery safety state prediction, battery fault diagnosis, battery system risk pre-warning, and safety control strategy research.This book is suitable for teachers and students of universities and colleges in the automotive and related industries, engineers and technicians in the R&amp;D departments of enterprises or automotive enthusiasts to read and refer.The basis of English translation of this book, originally in Chinese, was facilitated by artificial intelligence. The content was later revised by the author for accuracy.

Big Data Analysis and Application of Power Battery Operation Safety

This book covers computational group theory (CGT), an active, rapidly expanding field at the interface of mathematics and computer science. It is the first systematic effort to establish computing with infinite matrix groups on the same footing as the rest of CGT. The book emphasizes practical methods based on results of linear group theory, solutions of fundamental algorithmic problems, and provision of software. Additionally, the book sets out a research program to make further progress in the area.

Linear Groups and Computation

Das bekannte „Balzert“ Lehrbuch der Softwaretechnik liegt nun in einer komplett überarbeiteten Version vor. Im Vergleich zu den voluminösen früheren drei Bänden liegt hier eine Referenz für den täglichen Gebrauch in einem Band vor, der das gesamte Wissen der Softwaretechnik von den Grundlagen und Prinzipien über die Aktivitäten der Entwicklung bis zum Management umfasst.&nbsp;Softwaretechnik ist Grundlage und Voraussetzung für erfolgreiche Softwareentwicklung. Professionelles Arbeiten reduziert Projektrisiken und ist angesichts der stark wachsenden Schadensfälle zwingend, um vor Gericht zu bestehen. Die zwei Autoren Helmut Balzert und Christof Ebert verknüpfen Erfahrungen aus Universität und Praxis. Sie zeigen, dass strukturiertes Arbeiten kein Hemmschuh ist, sondern Voraussetzung für gute Software-Systeme. Das Buch adressiert als Lehrbuch mit systematischen Grundlagen Studierende, aber als Nachschlagewerk auch den Praktiker im Unternehmen und Start-Up. Die vielen Beispiele und Praxis-Tipps machen die Stofffülle verdaulich und zeigen unmittelbar, wie sie sich umsetzen lässt. Themen wie agile Entwicklung, Service-orientierte Entwicklung, Cybersecurity, Continuous X, Open Source und weltweit verteilte Teams unterstreichen die aktuelle Relevanz. Dabei balancieren die beiden Autoren gekonnt zwischen nötigen Grundlagen, die fast zeitlos sind, und moderner Softwareentwicklung, ohne sich in Technologie-Hypes und Modetrends zu verlieren. Das Lehrbuch adressiert unterschiedliche Arten von Software aus der Praxis und für die Praxis. Von der Anwendungs-Entwicklung, über klassische Unternehmens-IT, hin zu eingebetteter Echtzeit-Software, bis zu Spezial-Themen wie Gaming und Low-Code. Ein komplett zweisprachiges Glossar hilft zum Verständnis der vielen Fachbegriffe.Das Buch wendet sich an Studierende, Software-Entwickelnde, Führungskräfte und Forschende. Es kann zur Vorlesungsbegleitung, zum Selbststudium und zum Nachschlagen verwendet werden. Der Inhalt<ul><li>Die Wissenschaftsdisziplin Softwaretechnik</li><li>Basistechniken</li><li>Basiskonzepte</li><li>Requirements Engineering</li><li>Der Entwurf</li><li>Die Implementierung</li><li>Verteilung, Installation, Abnahme und Einführung</li><li>Der Betrieb</li><li>Allgemeines Management</li><li>Softwaremanagement</li><li>Prozess- und Qualitätsmodelle</li></ul> Die AutorenHelmut Balzert war Inhaber des Lehrstuhls für Software-Technik an der Ruhr-Universität Bochum. Forschungsgebiete: Web Engineering, E-Learning, generierende Systeme, Software-Ergonomie. Er ist Autor und Mitautor von mehr als 40 Büchern. Christof Ebert studierte Elektrotechnik und Informatik an der Universität Stuttgart sowie an der Kansas State University. Er ist Geschäftsführer der Vector Consulting Services und Lehrbeauftragter an der Universität Stuttgart sowie im Vorstand des Fachbereichs „Softwaretechnik“ der Gesellschaft für Informatik. Als Autor ist er Verfasser verschiedener Fachbücher sowie einiger wissenschaftlichen Publikationen.&nbsp;

Lehrbuch der Softwaretechnik

Principles, Algorithms, and Applications of the Free Energy Principle for Engineers

Fundamentals of Active Inference

Retrieval-Augmented Generation (RAG) represents the cutting edge of AI innovation, bridging the gap between large language models (LLMs) and real-world knowledge. This book provides the definitive roadmap for building, optimizing, and deploying enterprise-grade RAG systems that deliver measurable business value.
This comprehensive guide takes you beyond basic concepts to advanced implementation strategies, covering everything from architectural patterns to production deployment. You'll explore proven techniques for document processing, vector optimization, retrieval enhancement, and system scaling, supported by real-world case studies from leading organizations.
Key Learning Objectives
<ul>
<li>Design and implement production-ready RAG architectures for diverse enterprise use cases</li>
<li>Master advanced retrieval strategies including graph-based approaches and agentic systems</li>
<li>Optimize performance through sophisticated chunking, embedding, and vector database techniques</li>
<li>Navigate the integration of RAG with modern LLMs and generative AI frameworks</li>
<li>Implement robust evaluation frameworks and quality assurance processes</li>
<li>Deploy scalable solutions with proper security, privacy, and governance controls</li>
</ul>
Real-World Applications
<ul>
<li>Intelligent document analysis and knowledge extraction</li>
<li>Code generation and technical documentation systems</li>
<li>Customer support automation and decision support tools</li>
<li>Regulatory compliance and risk management solutions</li>
</ul>
Whether you're an AI engineer scaling existing systems or a technical leader planning next-generation capabilities, this book provides the expertise needed to succeed in the rapidly evolving landscape of enterprise AI.
What You Will Learn
<ul>
<li>Architecture Mastery: Design scalable RAG systems from prototype to enterprise production</li>
<li>Advanced Retrieval: Implement sophisticated strategies, including graph-based and multi-modal approaches</li>
<li>Performance Optimization: Fine-tune embedding models, vector databases, and retrieval algorithms for maximum efficiency</li>
<li>LLM Integration: Seamlessly combine RAG with state-of-the-art language models and generative AI frameworks</li>
<li>Production Excellence: Deploy robust systems with monitoring, evaluation, and continuous improvement processes</li>
<li>Industry Applications: Apply RAG solutions across diverse sectors, including finance, healthcare, legal, and technology</li>
</ul>

Advanced Techniques and Production-Ready Solutions for Enterprise AI

Mastering Retrieval-Augmented Generation

Generalized Matrix Inversion: A Machine Learning Approach

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