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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Scientific Study from the year 2025 in the subject Computer Sciences - Artificial Intelligence, , language: English, abstract: In recent times, developments in artificial intelligence (AI) and machine learning (ML) have propelled improvements in systems and control engineering. We exist in a time of extensive data, where AI and ML can evaluate large volumes of information instantly to enhance efficiency and precision in decisions based on data. In control engineering, for instance, AI algorithms can anticipate system behaviors and autonomously modify controls to enhance performance for better efficiency and dependability. ML models, with their ability to learn, consistently enhance their predictions and choices as they handle additional data, enabling systems to dynamically adjust to evolving environments and operational circumstances. This swift adjustment enhances the functions of current systems and enables the creation of groundbreaking solutions, like self-driving cars and intelligent power grids, which were previously deemed unfeasible.The rapid expansion of digital data has propelled significant advancements in Big Data analytics, Machine Learning, and Deep Learning. These technologies are increasingly integrated across industries, facilitating automated decision-making, predictive modeling, and advanced pattern recognition. This chapter provides an in-depth review of recent progress in these domains, emphasizing breakthroughs in scalable data processing frameworks, cloud and edge computing, AutoML, explainable AI, transformer architectures, self-supervised learning, and generative models. Furthermore, it explores key applications in healthcare, finance, and autonomous systems, along with challenges such as data privacy, ethical concerns, and computational constraints. The discussion concludes with future directions, highlighting the potential of federated learning, neuromorphic computing, and novel algorithmic improvements to further expand AI's impact across disciplines.…

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Buch. Zustand: Neu. Neuware - A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementationRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process.Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume:\* Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk\* Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques\* Covers the basic principles and nuances of feature engineering and common machine learning algorithms\* Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle\* Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitionersRisk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.…

Taschenbuch. Zustand: Neu. Neuware - Modeling Intelligence Systems: Build a Strong Mathematical Foundation for Artificial Intelligence, Machine Learning, Deep Learning, and Modern AI Applications Have you ever wondered what truly powers ChatGPT, autonomous vehicles, recommendation systems, facial recognition, or modern AI assistants Behind every intelligent system lies mathematics. If you've tried learning AI before and felt overwhelmed by complex equations or disconnected theory, you're not alone. The good news is that understanding the mathematics behind AI doesn't have to be intimidating-it simply requires the right guide. Modeling Intelligence Systems is a practical, beginner-friendly guide that explains the mathematical principles behind Artificial Intelligence, Machine Learning, and Deep Learning using clear language, intuitive explanations, and real-world examples. Rather than asking you to memorize formulas, this book helps you understand why they matter and how they are used to build intelligent systems. Whether you're a student, software developer, data scientist, AI enthusiast, or working professional, you'll develop the confidence to understand modern AI from the ground up. Inside this book, you'll learn the essential mathematics that powers today's AI systems, including linear algebra, calculus, probability, statistics, optimization, feature engineering, neural networks, deep learning, transformers, large language models, computer vision, reinforcement learning, recommendation systems, AI agents, model deployment, responsible AI, and the future of intelligent systems. Each topic is explained with practical examples, mathematical intuition, working code samples where appropriate, and exercises that reinforce your understanding. By the time you finish this book, you'll be able to read AI literature with greater confidence, understand how modern algorithms learn from data, make better technical decisions, and build a strong foundation for advanced topics in machine learning and artificial intelligence. Instead of relying solely on libraries and frameworks, you'll understand the mathematical ideas that make them work. Artificial intelligence is transforming software development, healthcare, finance, cybersecurity, education, manufacturing, robotics, and countless other industries. The professionals who understand both the theory and the practical application of AI will be best positioned to take advantage of these opportunities. Building that foundation today will prepare you for the technologies shaping tomorrow. If you're ready to move beyond using AI tools and start understanding the mathematics that powers them, scroll up and click 'Buy Now' to begin building a solid foundation in artificial intelligence today.…

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Taschenbuch. Zustand: Neu. Neuware.

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