Practical Artificial Intelligence Systems
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Practical Artificial Intelligence Systems
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Author : Nick Cercone
language : en
Publisher: Pergamon Press
Release Date : 1985-08-01
Practical Artificial Intelligence Systems written by Nick Cercone and has been published by Pergamon Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985-08-01 with Artificial intelligence categories.
A Practical Approach For Building Real World Ai Systems Using Deep Learning
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Author : Dr. Vidya Rajasekaran
language : en
Publisher: IIP Iterative International Publishers
Release Date : 2025-03-17
A Practical Approach For Building Real World Ai Systems Using Deep Learning written by Dr. Vidya Rajasekaran and has been published by IIP Iterative International Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-17 with Computers categories.
Artificial Intelligence (AI) has emerged as a transformative force, reshaping industries, enhancing decision-making, and fostering innovation in countless domains. At the heart of this revolution lies deep learning, a powerful subset of machine learning that enables AI systems to learn and perform complex tasks with remarkable accuracy. While the theoretical foundations of deep learning are well-established, bridging the gap between theory and practical implementation in real-world applications remains a significant challenge. This book, A Practical Approach for Building Real-World AI Systems using Deep Learning Techniques, is designed to guide researchers, practitioners, and enthusiasts through the nuanced journey of developing AI solutions that address real-world challenges. The focus is on practical, hands-on techniques that blend foundational concepts with modern tools and methodologies. Each chapter has been meticulously structured to present key concepts, demonstrate their application through real-world examples, and provide practical coding implementations. The chapters are not limited to, 1. Introduction of Deep Learning Techniques 2. Training and Optimizing Deep Learning Models 3. Designing Neural Network Architectures 4. Convolutional Neural Networks (CNNs) for Image Classification and Predictions 5. RNNs, LSTMs, and Transformers for Text and Sentiment Classifications 6. Real-World Applications of AI Systems 7. Ethical Considerations and Bias in AI 8. Exploring Advanced Topics in Deep Learning 9. Case Studies and Applications of Deep Learning 10. Challenges and Future Trends of Deep Learning
Intelligent Systems For Engineers And Scientists
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Author : Adrian A. Hopgood
language : en
Publisher: CRC Press
Release Date : 2021-12-09
Intelligent Systems For Engineers And Scientists written by Adrian A. Hopgood and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-09 with Technology & Engineering categories.
The fourth edition of this bestselling textbook explains the principles of artificial intelligence (AI) and its practical applications. Using clear and concise language, it provides a solid grounding across the full spectrum of AI techniques, so that its readers can implement systems in their own domain of interest. The coverage includes knowledge-based intelligence, computational intelligence (including machine learning), and practical systems that use a combination of techniques. All the key techniques of AI are explained—including rule-based systems, Bayesian updating, certainty theory, fuzzy logic (types 1 and 2), agents, objects, frames, symbolic learning, case-based reasoning, genetic algorithms and other optimization techniques, shallow and deep neural networks, hybrids, and the Lisp, Prolog, and Python programming languages. The book also describes a wide range of practical applications in interpretation and diagnosis, design and selection, planning, and control. Fully updated and revised, Intelligent Systems for Engineers and Scientists: A Practical Guide to Artificial Intelligence, Fourth Edition features: A new chapter on deep neural networks, reflecting the growth of machine learning as a key technique for AI A new section on the use of Python, which has become the de facto standard programming language for many aspects of AI The rule-based and uncertainty-based examples in the book are compatible with the Flex toolkit by Logic Programming Associates (LPA) and its Flint extension for handling uncertainty and fuzzy logic. Readers of the book can download this commercial software for use free of charge. This resource and many others are available at the author’s website: adrianhopgood.com. Whether you are building your own intelligent systems, or you simply want to know more about them, this practical AI textbook provides you with detailed and up-to-date guidance.
Generative Ai System Design
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Author : Anand Vemula
language : en
Publisher: Independently Published
Release Date : 2024-06-26
Generative Ai System Design written by Anand Vemula and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-26 with Computers categories.
"Generative AI System Design: A Practical Guide" offers a comprehensive exploration of designing and implementing generative artificial intelligence systems. This book serves as an essential resource for both beginners and experienced professionals looking to delve into the world of generative AI with a focus on practical applications and real-world scenarios. The book begins with an introduction to generative AI, covering its historical background, key applications across various industries, and the foundational principles underlying generative models. Readers will gain a solid understanding of machine learning basics, deep dive into probabilistic models, neural networks, and explore advanced techniques such as autoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs), and flow-based models. A significant portion of the book is dedicated to advanced topics in generative AI, including reinforcement learning for generative models, self-supervised learning, transfer learning, and multi-modal generative models. Special attention is given to generative AI system design principles, covering system architecture, data management, model training, scalability, performance optimization, and integration with existing systems. The book provides hands-on tutorials with complete solutions, code examples, case studies from healthcare, finance, art, and gaming industries, and practical exercises to reinforce learning. It emphasizes performance optimization techniques such as model compression, efficient training methods, hardware acceleration using GPUs and TPUs, and strategies for reducing inference latency. Furthermore, "Generative AI System Design: A Practical Guide" addresses deployment strategies, monitoring, continuous learning, and maintenance of generative AI systems in production environments. It explores DevOps practices tailored for generative AI, including continuous integration and deployment, infrastructure as code, automated testing, monitoring, and ensuring scalability and high availability. This guide concludes with insights into emerging trends, innovations in model architectures, the future of work with generative AI, and societal impacts. It aims to equip readers with the knowledge and skills to design, deploy, and optimize generative AI systems effectively.
Practical Ai Security
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Author : Harriet Farlow
language : en
Publisher: No Starch Press
Release Date : 2026-06-09
Practical Ai Security written by Harriet Farlow and has been published by No Starch Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2026-06-09 with Computers categories.
A forward-looking primer on how AI models and systems work, the attacks that can disrupt them, and what security measures the industry uses to keep them safe. Artificial intelligence now underpins everything from chatbots to national infrastructure, but with new capability comes new risk. Attacks like prompt injection, data poisoning, and model theft are already targeting the systems we rely on. Practical AI Security is a comprehensive foundation to the field—a 0-to-60 guide to everything you need to know at the intersection of AI and cybersecurity. Drawing real-world experience securing deployed systems, Harriet Farlow demystifies how modern AI works, why it’s vulnerable, and how to protect it. You’ll learn how AI systems differ from machine learning models, why that matters for security, and how to defend both. Through clear explanations, real-world examples, and over 30 hands-on Python demos, you will: Understand how different kinds of machine learning models—from computer vision and language models to signal models—are built and how their architectures create unique vulnerabilities Explore how these models are integrated into more autonomous, agentic AI systems, and why deployment introduces new weaknesses and risks Identify, exploit, and defend against dozens of weaknesses and attacks across the AI lifecycle, including data poisoning, model theft, and prompt injection Use industry frameworks such as OWASP and MITRE ATLAS to threat model different types of AI systems Design and execute AI-specific red teaming campaigns, and understand what makes them distinct from traditional security tests Examine how AI itself can be weaponized in cybersecurity, including cases where AI attacks other AI Build robust frameworks for AI risk management, assurance, and testing Bridge technical and policy perspectives to strengthen AI security culture across organizations Covering fundamentals through to advanced topics—from adversarial machine learning and red teaming to risk management, governance, and AI safety—this book turns theory into skill. Even if you don’t think you’re technical now, you’ll finish with practical confidence and a security mindset. Whether you use, build, deploy, or oversee AI, this isn’t niche knowledge—it’s the foundation for defending the technologies that will define the next era of human progress.
Practical Ai For Cybersecurity
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Author : Ravi Das
language : en
Publisher: CRC Press
Release Date : 2021-02-26
Practical Ai For Cybersecurity written by Ravi Das and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Computers categories.
The world of cybersecurity and the landscape that it possesses is changing on a dynamic basis. It seems like that hardly one threat vector is launched, new variants of it are already on the way. IT Security teams in businesses and corporations are struggling daily to fight off any cyberthreats that they are experiencing. On top of this, they are also asked by their CIO or CISO to model what future Cyberattacks could potentially look like, and ways as to how the lines of defenses can be further enhanced. IT Security teams are overburdened and are struggling to find ways in order to keep up with what they are being asked to do. Trying to model the cyberthreat landscape is a very laborious process, because it takes a lot of time to analyze datasets from many intelligence feeds. What can be done to accomplish this Herculean task? The answer lies in Artificial Intelligence (AI). With AI, an IT Security team can model what the future Cyberthreat landscape could potentially look like in just a matter of minutes. As a result, this gives valuable time for them not only to fight off the threats that they are facing, but to also come up with solutions for the variants that will come out later. Practical AI for Cybersecurity explores the ways and methods as to how AI can be used in cybersecurity, with an emphasis upon its subcomponents of machine learning, computer vision, and neural networks. The book shows how AI can be used to help automate the routine and ordinary tasks that are encountered by both penetration testing and threat hunting teams. The result is that security professionals can spend more time finding and discovering unknown vulnerabilities and weaknesses that their systems are facing, as well as be able to come up with solid recommendations as to how the systems can be patched up quickly.
The Artificial Intelligence Experience
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Author : Susan J. Scown
language : en
Publisher: Digital Press
Release Date : 1985
The Artificial Intelligence Experience written by Susan J. Scown and has been published by Digital Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985 with Computers categories.
Explainable Artificial Intelligence A Practical Guide
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Author : Parikshit Narendra Mahalle
language : en
Publisher: CRC Press
Release Date : 2024-12-02
Explainable Artificial Intelligence A Practical Guide written by Parikshit Narendra Mahalle and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-02 with Computers categories.
This book explores the growing focus on artificial intelligence (AI) systems in both industry and academia. It evaluates and justifies AI applications while enhancing trust in AI outcomes and aiding comprehension of AI feature development. Key topics include an overview of explainable AI, black box model understanding, interpretability techniques, practical XAI applications, and future trends and challenges in XAI. Technical topics discussed in the book include: Explainable AI overview Understanding black box models Techniques for model interpretability Practical applications of XAI Future trends and challenges in XAI
Intelligent Systems From Theory To Applications
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Author : Oleksandr Kuznetsov
language : en
Publisher: Springer Nature
Release Date : 2025-10-11
Intelligent Systems From Theory To Applications written by Oleksandr Kuznetsov and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-11 with Computers categories.
The field of Artificial Intelligence has seen explosive growth in recent years, yet a persistent challenge remains, namely bridging the gap between theoretical concepts and practical implementation. Too often, students encounter either highly abstract mathematical treatments disconnected from real-world applications, or simplified implementations that fail to convey the underlying principles. This textbook directly addresses this challenge through its unique approach combining clear theoretical explanations with comprehensive Python implementations. Drawing from the author’s extensive experience teaching at the University of eCampus, Italy, this book provides a thorough exploration of intelligent systems, covering classical approaches to cutting-edge techniques. Organized into three main areas, the book explores the foundations of intelligent systems, examines optimization and search methods that form the backbone of AI solutions, and ends by investigating machine learning fundamentals that enable systems to derive knowledge from experience. A distinguishing feature of this work is its practical approach. Each theoretical concept is paired with Python implementations and exercises. This hands-on methodology develops both conceptual understanding and practical skills simultaneously. The exercises progress from basic implementations to complex real-world problems. The textbook aims to serve both undergraduate and graduate students in computer science, engineering, and related disciplines. It assumes basic programming knowledge but introduces concepts progressively. Professionals implementing intelligent systems will also find valuable insights and practical guidance. Despite AI’s rapid evolution, this book provides both current knowledge and the conceptual framework necessary for understanding future developments. Ethical considerations are addressed throughout, encouraging critical thinking about responsible AI implementation. It is the author’s hope that this book will be a valuable resource in the reader’s journey to understand and design intelligent systems.
Practical Artificial Intelligence
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Author : Arnaldo Pérez Castaño
language : en
Publisher: Apress
Release Date : 2018-05-23
Practical Artificial Intelligence written by Arnaldo Pérez Castaño and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-23 with Computers categories.
Discover how all levels Artificial Intelligence (AI) can be present in the most unimaginable scenarios of ordinary lives. This book explores subjects such as neural networks, agents, multi agent systems, supervised learning, and unsupervised learning. These and other topics will be addressed with real world examples, so you can learn fundamental concepts with AI solutions and apply them to your own projects. People tend to talk about AI as something mystical and unrelated to their ordinary life. Practical Artificial Intelligence provides simple explanations and hands on instructions. Rather than focusing on theory and overly scientific language, this book will enable practitioners of all levels to not only learn about AI but implement its practical uses. What You’ll Learn Understand agents and multi agents and how they are incorporated Relate machine learning to real-world problems and see what it means to you Apply supervised and unsupervised learning techniques and methods in the real world Implement reinforcement learning, game programming, simulation, and neural networks Who This Book Is For Computer science students, professionals, and hobbyists interested in AI and its applications.