Informatyka
Zajrzyj do kategorii Informatyka w księgarni internetowej Ebookpoint. Znajdziesz tutaj bestsellerowe książki, ebooki i kursy video z branży IT. Sięgnij po najlepszą literaturę dla specjalistów i rozwijaj doświadczenie, które już posiadasz, lub rozpocznij swoją przygodę z programowaniem, cyberbezpieczeństwem lub grafiką komputerową. Pogłębiaj swoją wiedzę tak, jak Ci wygodnie - z tradycyjną książką, wygodnym ebookiem lub nowoczesnym videokursem. Sprawdź, jakie tytuły znajdziesz w kategorii Informatyka!
Artificial Intelligence and Expert Systems. Techniques and Applications for Problem Solving
Mercury Learning and Information, I. Gupta, G....
This book covers current AI applications and techniques for solving problems and accomplishing tasks. It introduces branches of AI such as formal logic, reasoning, knowledge engineering, expert systems, neural networks, and fuzzy logic. It emphasizes expert systems, with sections on state space search, knowledge engineering, neural networks, fuzzy logic, and Prolog.It begins with an introduction to AI and its applications, setting the stage for foundational concepts. Readers are guided through state space search and heuristic search strategies, crucial for problem-solving in AI. The focus shifts to expert systems, covering their development life cycle, knowledge acquisition, and representation, providing a deep dive into emulating human decision-making.Later chapters cover neural networks and the learning process, essential for creating adaptive systems. Sections on fuzzy logic and fuzzy systems introduce methods for handling uncertainty in AI. Final chapters on programming in logic and advanced Prolog offer practical techniques for AI solutions. This approach equips readers with the skills to apply AI in various domains, enhancing their problem-solving abilities and understanding of intelligent systems.
Zsolt Nagy
Machine learning and neural networks are pillars on which you can build intelligent applications. Artificial Intelligence and Machine Learning Fundamentals begins by introducing you to Python and discussing AI search algorithms. You will cover in-depth mathematical topics, such as regression and classification, illustrated by Python examples.As you make your way through the book, you will progress to advanced AI techniques and concepts, and work on real-life datasets to form decision trees and clusters. You will be introduced to neural networks, a powerful tool based on Moore's law.By the end of this book, you will be confident when it comes to building your own AI applications with your newly acquired skills!
Artificial Intelligence Basics. A Self-Teaching Introduction
Mercury Learning and Information, N. Gupta, R....
This book is designed as a self-teaching introduction to the fundamental concepts of artificial intelligence (AI). It begins with the history of AI, the Turing test, and early applications, providing a strong foundation. Later chapters cover the basics of searching, game playing, and knowledge representation. The journey continues with detailed explorations of expert systems and machine learning, equipping readers with essential AI techniques.As the course progresses, you will delve into separate programming chapters on Prolog and Python, learning how to implement AI concepts in these languages. These chapters offer practical coding experience, enhancing your understanding of AI programming. The book culminates with a comprehensive chapter on AI machines and robotics, showcasing numerous modern applications and providing a glimpse into the future of AI technology.Understanding these AI concepts is crucial as they form the basis of many modern technologies and applications. This book ensures a smooth transition from a beginner to a proficient AI practitioner, equipped with both theoretical knowledge and practical skills. By the end of the book, you will have a thorough understanding of AI's history, core principles, and practical implementations, ready to apply this knowledge to real-world problems and projects.
Denis Rothman, Nicolas GUET, Jérémie ROTHMAN
Artificial intelligence has the potential to replicate humans in every field. Artificial Intelligence By Example serves as a starting point for you to understand how AI is built, with the help of intriguing examples and case studies.Artificial Intelligence By Example will make you an adaptive thinker and help you apply concepts to real-life scenarios. Using some of the most interesting AI examples, right from a simple chess engine to a cognitive chatbot, you will learn how to tackle the machine you are competing with. You will study some of the most advanced machine learning models, understand how to apply AI to blockchain and IoT, and develop emotional quotient in chatbots using neural networks.You will move on to designing AI solutions in a simple manner rather than get confused by complex architectures and techniques. This comprehensive guide will be a starter kit for you to develop AI applications on your own.By the end of this book, you will have understood the fundamentals of AI and worked through a number of case studies that will help you develop your business vision.
Artificial Intelligence Engines. A Tutorial Introduction to the Mathematics of Deep Learning
James V Stone
This book is a comprehensive guide to the mathematics behind artificial intelligence engines, taking readers from foundational concepts to advanced applications. It begins with an introduction to artificial neural networks, exploring topics like perceptrons, linear associative networks, and gradient descent. Practical examples accompany each chapter, making complex mathematical principles accessible, even for those with limited prior knowledge.The book's detailed structure covers key algorithms like backpropagation, Hopfield networks, and Boltzmann machines, advancing to deep restricted Boltzmann machines, variational autoencoders, and convolutional neural networks. Modern topics such as generative adversarial networks, reinforcement learning, and capsule networks are explored in depth. Each section connects theory to real-world AI applications, helping readers understand how these techniques are used in practice.Ideal for students, researchers, and AI enthusiasts, the book balances theoretical depth with practical insights. Basic mathematical knowledge or foundation is recommended, allowing readers to fully engage with the content. This book serves as an accessible yet thorough resource for anyone eager to dive deeper into artificial intelligence and machine learning.
Artificial Intelligence. Ethical, social, and security impacts for the present and the future
IT Governance Publishing, Dr. Julie E. Mehan
This book offers an in-depth exploration of Artificial Intelligence (AI), from its origins to the ethical and societal challenges it presents today. It provides a comprehensive understanding of AI’s impact on human interaction, collaboration, privacy, and security. Through analyzing both opportunities and risks, the book emphasizes the ethical concerns surrounding AI, such as bias, privacy violations, and security threats.Chapters explore AI’s transformative role in cybersecurity, misinformation, and human-machine collaboration, highlighting its implications for job markets and human relationships. Real-world examples illustrate how AI can drive progress or cause harm. The ethical dilemmas around AI, including its use in surveillance and decision-making, are thoroughly examined, presenting challenges central to modern technology.Looking ahead, the book offers a forward-thinking perspective on AI’s future, discussing emerging trends and the need for responsible policy-making. It concludes by addressing how society can prepare for AI’s continued growth, offering strategies for navigating the evolving landscape. With practical insights and deep analysis, this book helps readers grasp AI’s profound implications for our future.
Anand Deshpande, Manish Kumar, Albenzo Coletta, Giancarlo...
In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data.With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems.By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.
Bojan Kolosnjaji, Huang Xiao, Peng Xu, Apostolis...
Artificial intelligence offers data analytics methods that enable us to efficiently recognize patterns in large-scale data. These methods can be applied to various cybersecurity problems, from authentication and the detection of various types of cyberattacks in computer networks to the analysis of malicious executables.Written by a machine learning expert, this book introduces you to the data analytics environment in cybersecurity and shows you where AI methods will fit in your cybersecurity projects. The chapters share an in-depth explanation of the AI methods along with tools that can be used to apply these methods, as well as design and implement AI solutions. You’ll also examine various cybersecurity scenarios where AI methods are applicable, including exercises and code examples that’ll help you effectively apply AI to work on cybersecurity challenges. The book also discusses common pitfalls from real-world applications of AI in cybersecurity issues and teaches you how to tackle them.By the end of this book, you’ll be able to not only recognize where AI methods can be applied, but also design and execute efficient solutions using AI methods.*Email sign-up and proof of purchase required
Francis X. Govers III
Artificial Intelligence for Robotics starts with an introduction to Robot Operating Systems (ROS), Python, robotic fundamentals, and the software and tools that are required to start out with robotics. You will learn robotics concepts that will be useful for making decisions, along with basic navigation skills.As you make your way through the chapters, you will learn about object recognition and genetic algorithms, which will teach your robot to identify and pick up an irregular object. With plenty of use cases throughout, you will explore natural language processing (NLP) and machine learning techniques to further enhance your robot. In the concluding chapters, you will learn about path planning and goal-oriented programming, which will help your robot prioritize tasks.By the end of this book, you will have learned to give your robot an artificial personality using simulated intelligence.
Francis X. Govers III, Dr. Kamesh Namuduri
Unlock the potential of your robots by enhancing their perception with cutting-edge artificial intelligence and machine learning techniques. From neural networks to computer vision, this second edition of the book equips you with the latest tools, new and expanded topics such as object recognition and creating artificial personality, and practical use cases to create truly smart robots.Starting with robotics basics, robot architecture, control systems, and decision-making theory, this book presents systems-engineering methods to design problem-solving robots with single-board computers. You'll explore object recognition using YOLO and genetic algorithms to teach your robot to identify and pick up objects, leverage natural language processing to give your robot a voice, and master neural networks to classify and separate objects and navigate autonomously, before advancing to guiding your robot arms using reinforcement learning and genetic algorithms. The book also covers path planning and goal-oriented programming to prioritize your robot's tasks, showing you how to connect all software using Python and ROS 2 for a seamless experience.By the end of this book, you'll have learned how to transform your robot into a helpful assistant with NLP and give it an artificial personality, ready to tackle real-world tasks and even crack jokes.
Tomasz Rymarczyk
This monograph aims to synthesize methods, measurement architectures, and algorithms that advance approaches to electrical and ultrasonic tomography, with a particular focus on artificial intelligence in image reconstruction and decision support. The work places these techniques in modern, complex environmental, industrial, and medical diagnostic systems, where non-invasive measurements are required for reliable observation, control, and process optimization. The scope of this work encompasses forward and inverse problems, numerical modelling, and data-driven learning methods, and is based on practical prototypes and verified applications. Tomographic imaging is presented as a family of techniques that infer internal structure based on boundary or remote measurements, enabling inspection without physical intervention. The theoretical foundations are outlined along with historical context and standard formulations of inverse problems, which are ill-posed and sensitive to noise and modelling errors. Established numerical frameworks, such as the Finite Element Method, are used to regularize and solve forward and inverse problems for electric and acoustic fields. These pillars provide a coherent path from physics to computation, and ultimately to images interpreted in an operational context. Artificial intelligence methods were applied to improve reconstruction fidelity, noise immunity, and computational efficiency. The text discusses deterministic frameworks such as Tikhonov, Gauss-Newton, and Total Variation, followed by a discussion of machine learning and deep learning architectures such as LSTM and CNN, along with ResNet, DiffNet, and specifically developed differential models for tomographic signals. The proposed multi-branch and pixel-centric strategies were evaluated using quantitative metrics such as RMSE, SSIM, ICC, Pearson correlation, relative image error, MAE, MAPE, and related metrics that reflect both perceptual and task-specific quality. The combination of physics-based modeling and prior knowledge has been shown to reduce inference time and increase noise tolerance compared to classical iterative solvers. A significant portion of the monograph is devoted to the design and evolution of measurement devices. Electrical and hybrid tomographs, next-generation ultrasound tomographs, a beamforming platform, and specialized flaw detection solutions are designed and characterized. Portable and mobile configurations, along with body potential mapping, are used to extend tomographic detection capabilities to include outpatient and situational monitoring. The measurement layer is integrated with distributed acquisition, synchronization, and embedded processing, allowing the systems to operate within industrial and clinical constraints. Applications in process engineering and medicine are presented. Fermentation control, crystallization monitoring, and autonomous process supervision illustrate industrial utility, including connections to the Internet of Things and real-time data infrastructure. Medical research includes non-invasive lung monitoring, portable diagnostics, and ultrasound brain detection, as well as portable hybrid ultrasound impedance solutions for lower urinary tract assessment. Non-destructive testing is addressed using advanced ultrasound imaging on the DefectoVision platform, which describes 3D reconstruction and quantitative assessment. These cases demonstrate that tomographic sensing can reveal internal states, detect anomalies, and support inspection without disrupting production or compromising safety. The book is designed to guide the reader from fundamentals to implementations and verified use cases. Chapter 1 introduces tomographic imaging, the physical principles underlying electrical and ultrasound techniques, and the challenges of the inverse problem. Chapter 2 discusses reconstruction methods, from deterministic regularization to machine learning and deep learning, along with evaluation metrics. Chapter 3 documents the designed measurement devices along with their electronics, sensor geometry, and system characteristics. Chapter 4 develops reconstruction processes based on simulated and experimental datasets and discusses comparative performance, including hybrid and 3D approaches. Chapter 5 consolidates applications in industrial processes and medical diagnostics, presenting experimental setups, results, and discussions that link quantitative metrics to operational requirements. Chapter 6 concludes with a summary, conclusions, and perspectives for further development. This publication is aimed at researchers and PhD students in the fields of sensors, inverse problems, and computational imaging, as well as engineers and practitioners responsible for process control, non-destructive testing, and medical technology assessment. The material was developed autonomously, with theoretical assumptions, numerical methods, device descriptions, and application studies, so that knowledge can be transferred from laboratory prototypes to real systems. This work was developed thanks to the research community and collaboration at the Netrix S.A. Research and Development Centre and the Institute of Information Technology and Innovative Technologies at the WSEI University in Lublin. Appreciation is expressed to my colleagues who collaborated with me on research projects in the areas of device prototyping, data acquisition, and algorithm development, which translated concepts into working systems. We also extend our gratitude to the reviewers, whose insightful comments contributed to improved clarity and completeness, and to our family for their continued support. The presented projects were developed to demonstrate how intelligent tomographic measurement systems can be constructed and deployed as reliable imaging, monitoring, and control tools. This synthesis of physics-based modelling and learning-based reasoning will be useful to both academia and industry seeking to implement practical, large-scale tomography.
Artificial Intelligence in the 21st Century. The Future of Technology and Human Innovation
Mercury Learning and Information, Stephen Lucci, Sarhan...
This third edition provides a comprehensive, accessible presentation of AI, including examples, applications, full-color images, and human interest boxes. New chapters on deep learning, AI security, and AI programming keep the content cutting-edge. Topics like neural networks, genetic algorithms, natural language processing, planning, and complex board games are covered.The course starts with an AI overview, moving through uninformed search, intelligent search methods, and game-based strategies. It delves into logic in AI, knowledge representation, production systems, uncertainty in AI, and expert systems. Middle chapters cover machine learning, neural networks, and deep learning. It continues with nature-inspired search methods, natural language processing, and automated planning, ending with robotics and advanced computer games.These AI concepts are crucial for developing sophisticated AI applications. This book transitions you from novice to proficient AI practitioner, equipped with practical skills and comprehensive knowledge. Companion files with resources, simulations, and figures enhance learning. By the end, you'll understand AI principles and applications, ready to tackle real-world challenges.
Mercury Learning and Information, Oswald Campesato
This book introduces AI, then explores machine learning, deep learning, natural language processing (NLP), and reinforcement learning. Readers learn about classifiers like logistic regression, k-NN, decision trees, random forests, and SVMs. It delves into deep learning architectures such as CNNs, RNNs, LSTMs, and autoencoders, with Keras-based code samples supplementing the theory.Starting with a foundational AI overview, the course progresses into machine learning, explaining classifiers and their applications. It continues with deep learning, focusing on architectures like CNNs and RNNs. Advanced topics include LSTMs and autoencoders, essential for modern AI. The book also covers NLP and reinforcement learning, emphasizing their importance.Understanding these concepts is vital for developing advanced AI systems. This book transitions you from beginner to proficient AI practitioner, combining theoretical knowledge and practical skills. Appendices on Keras, TensorFlow 2, and Pandas enrich the learning experience. By the end, readers will understand AI principles and be ready to apply them in real-world scenarios.
Prateek Joshi
Artificial Intelligence is becoming increasingly relevant in the modern world. By harnessing the power of algorithms, you can create apps which intelligently interact with the world around you, building intelligent recommender systems, automatic speech recognition systems and more.Starting with AI basics you'll move on to learn how to develop building blocks using data mining techniques. Discover how to make informed decisions about which algorithms to use, and how to apply them to real-world scenarios. This practical book covers a range of topics including predictive analytics and deep learning.
Álvaro Morena Alberola, Gonzalo Molina Gallego, Unai...
Artificial Vision and Language Processing for Robotics begins by discussing the theory behind robots. You'll compare different methods used to work with robots and explore computer vision, its algorithms, and limits. You'll then learn how to control the robot with natural language processing commands. You'll study Word2Vec and GloVe embedding techniques, non-numeric data, recurrent neural network (RNNs), and their advanced models. You'll create a simple Word2Vec model with Keras, as well as build a convolutional neural network (CNN) and improve it with data augmentation and transfer learning. You'll study the ROS and build a conversational agent to manage your robot. You'll also integrate your agent with the ROS and convert an image to text and text to speech. You'll learn to build an object recognition system using a video.By the end of this book, you'll have the skills you need to build a functional application that can integrate with a ROS to extract useful information about your environment.
Arystoteles na nowo odczytany. Ryszarda Kilvingtona
Elżbieta Jung
Książka jest efektem wieloletnich badań dotyczących historii nauki średniowiecznej, mianowicie początków fizyki matematycznej. Można w niej wyodrębnić dwie części: monografię, w której autorka odpowiada na pytanie, czy XIV-wieczna fizyka matematyczna, inspirowana nominalistyczną filozofią Wilhelma Ockhama, doprowadziła do zerwania z jakościową fizyką Arystotelesa już w wieku XIV, oraz – tłumaczenie Kwestii o ruchu Ryszarda Kilvingtona, jednego z twórców szkoły Oksfordzkich Kalkulatorów. Z pozycji nominalistycznej, z jednej strony odrzuca on dużą część podejmowanej przez Arystotelesa problematyki, z drugiej zaś, wypełniając obowiązki nauczycielskie na średniowiecznym uniwersytecie, poddaje teorie Stagiryty interpretacji pozwalającej odnaleźć w jego myśli elementy bliskie – jak się Kilvingtonowi wydaje – jego poglądom. W swoich kwestiach Kilvington podejmuje analizę zagadnienia zmian, rozumianych, zgodnie z definicją Arystotelesa, jako ruch przestrzenny, zmiany jakościowe oraz ilościowe w ujęciu nominalistycznym, czyniąc matematykę właściwym językiem opisu przyrody. Są one też rezultatem jego nauczania, czasami zapisu ćwiczeń odbywanych w klasie ze studentami, zatem tekst ten ma również walor historyczny, ponieważ pokazuje nam, jak wielkie wymagania stawiano studentom średniowiecznym i jak dobrze się z nich wywiązywali. Jakiego rodzaju były to „obliczenia” i jakie dzięki nim można osiągnąć rezultaty, Czytelnik dowie się z lektury książki.