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Название: Demystifying Artificial Intelligence: Symbolic, Data-Driven, Statistical and Ethical AI
Автор: Emmanuel Gillain
Издательство: De Gruyter
Год: 2024
Страниц: 476
Язык: английский
Формат: pdf (true), epub
Размер: 34.1 MB

This book is intended for business professionals that want to understand the fundamental concepts of Artificial Intelligence (AI), their applications and limitations. Built as a collaborative effort between academia and the industry, this book bridges the gap between theory and business application, demystifying AI through fundamental concepts and industry examples. The reader will find here an overview of the different AI techniques to search, plan, reason, learn, adapt, understand and interact. The book covers the two traditional paradigms in AI: the statistical and data-driven AI systems, which learn and perform by ingesting millions of data points into Machine Learning algorithms, and the consciously modelled AI systems, known as symbolic AI systems, which use explicit symbols to represent the world and make conclusions. Rather than opposing those two paradigms, the book will also show how those different fields can complement each other. Out of the different fields of AI, much of the attention over the last years has been focused on the field of Machine Learning (ML), its subfield artificial neural network with Deep Learning, and natural language processing (NLP).

Название: The De Gruyter Handbook of Artificial Intelligence, Identity and Technology Studies
Автор: Anthony Elliott
Издательство: De Gruyter
Год: 2024
Страниц: 316
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

The De Gruyter Handbook of Artificial Intelligence, Identity and Technology Studies examines the relationship of the social sciences to Artificial Intelligence, surveying the various convergences and divergences between science and technology studies on the one hand and identity transformations on the other. It provides representative coverage of all aspects of the AI revolution, from employment to education to military warfare, impacts on public policy and governance and the future of ethics. How is AI currently transforming social, economic, cultural and psychological processes? This handbook answers these questions by looking at recent developments in supercomputing, Deep Learning and neural networks, including such topics as AI mobile technology, social robotics, Big Data and digital research. It focuses especially on mechanisms of identity by defining AI as a new context for self-exploration and social relations and analyzing phenomena such as race, ethnicity and gender politics in human-machine interfaces.

Название: PHP and Algorithmic Thinking for the Complete Beginner, 3rd Edition
Автор: Aristides S Bouras
Издательство: Independently published
Год: 2024
Страниц: 1409
Язык: английский
Формат: epub (true)
Размер: 10.1 MB

Completely updated for the latest version of PHP, this book offers a comprehensive introduction to programming, assuming no prior knowledge of the subject. It is designed for individuals, eager to learn PHP from scratch, providing a strong foundation in Algorithmic Thinking—the fundamental skill every aspiring programmer must acquire. Algorithmic Thinking encompasses more than just writing code; it entails mastering the art of problem-solving through coding. This edition retains all the popular features of its predecessor while introducing a wealth of new exercises, along with extensive revisions and updates. Furthermore, brand-new chapters offer a practical introduction to working with text files. Structured for both classroom use and independent study, each chapter is concluded with a set of questions and exercises designed to reinforce your understanding and apply what you've learned. With approximately 250 solved and 480 unsolved exercises, 500 true/false questions, 150 multiple-choice questions, and 200 review questions and crosswords (with solutions and answers available online). Notice: This book is exclusively focused on developing command-line interface (CLI) applications.

Название: Artificial Intelligence and Brain Research: Neural Networks, Deep Learning and the Future of Cognition
Автор: Patrick Krauss
Издательство: Springer
Год: 2024
Страниц: 258
Язык: английский
Формат: pdf (true), epub
Размер: 15.3 MB

How does Artificial Intelligence (AI) work and are there parallels to the human brain? What do natural and artificial intelligence have in common, and what are the differences? Is the brain nothing more than a biological computer? What are neural networks and how can the term Deep Learning be explained simply? Since the cognitive revolution in the middle of the last century, AI and brain research have been closely intertwined. There have been several spectacular breakthroughs in the field of AI in recent years, from alphaGo to DALL-E 2 and ChatGPT, which were completely unthinkable until recently. However, researchers are already working on the innovations of tomorrow, such as hybrid machine learning or neuro-symbolic AI. But what does this actually mean? Based on current research findings and exciting practical examples, this non-fiction book provides an understandable introduction to the basics and challenges of these fascinating disciplines. You will learn what neuroscience and psychology know about how the brain works and how artificial intelligence works. You will also learn how AI has revolutionized our understanding of the brain and how findings from brain research are used in computer science to further develop AI algorithms. Discover the fascinating world of these two disciplines. Find out why Artificial Intelligence and brain research are two sides of the same coin and how they will shape our future. Recently, Artificial Intelligence (AI) and Machine Learning have been making their way into more and more areas, such as medicine, science, education, finance, engineering, entertainment, and even art and music, and are becoming ubiquitous in twenty-first-century life. Many of the algorithms, design principles, and concepts used in AI today, such as neural networks or the aforementioned reinforcement learning, have their origins in biology and psychology. Therefore, neuroscience lectures are becoming an integral part of courses such as Computer Science or Artificial Intelligence at more and more universities.

Название: IOT Интернет вещей
Автор: Джейд Картер
Издательство: Литрес
Год: 2024
Страниц: 180
Формат: pdf
Язык: русский
Размер: 10,1 Мб

Книга "Интернет вещей (IoT): Разработка, Интеграция и Управление Устройствами" является руководством по изучению и применению технологий IoT на практике. Она охватывает основные аспекты разработки устройств, включая работу с популярными платформами Arduino и Raspberry Pi, интеграцию различных устройств и использование ключевых протоколов связи, таких как MQTT и CoAP. Также рассматриваются платформы управления IoT, такие как AWS IoT и Google Cloud IoT, и их применение в реальных проектах.

Название: CUDA Fortran for Scientists and Engineers: Best Practices for Efficient CUDA Fortran Programming 2nd Edition
Автор: Gregory Ruetsch, Massimiliano Fatica
Издательство: Morgan Kaufmann/Elsevier
Год: 2024
Страниц: 350
Язык: английский
Формат: epub
Размер: 157.2 MB

CUDA Fortran for Scientists and Engineers: Best Practices for Efficient CUDA Fortran Programming shows how high-performance application developers can leverage the power of GPUs using Fortran, the familiar language of scientific computing and supercomputer performance benchmarking. The authors presume no prior parallel computing experience, and cover the basics along with best practices for efficient GPU computing using CUDA Fortran. In order to add CUDA Fortran to existing Fortran codes, they explain how to understand the target GPU architecture, identify computationally-intensive parts of the code, and modify the code to manage the data and parallelism and optimize performance – all in Fortran, without having to rewrite in another language. Each concept is illustrated with actual examples so you can immediately evaluate the performance of your code in comparison. This second edition provides much needed updates on how to efficiently program GPUs in CUDA Fortran. It can be used either as a tutorial on GPU programming in CUDA Fortran as well as a reference text.

Название: Machine Learning and Python for Human Behavior, Emotion, and Health Status Analysis
Автор: Md Zia Uddin
Издательство: CRC Press
Год: 2025
Страниц: 264
Язык: английский
Формат: pdf (true)
Размер: 34.8 MB

This book is a practical guide for individuals interested in exploring and implementing smart home applications using Python. Comprising six chapters enriched with hands-on codes, it seamlessly navigates from foundational concepts to cutting-edge technologies, balancing theoretical insights and practical coding experiences. In short, it is a gateway to the dynamic intersection of Python programming, smart home technology, and advanced Machine Learning applications, making it an invaluable resource for those eager to explore this rapidly growing field. Covering Python basics, feature extraction, Deep Learning, and XAI, the book provides a comprehensive guide, offering an overall understanding of smart home applications.

Название: Hacker's Manual - 17th Edition 2024
Автор: Jon White (Editor)
Издательство: Future Publishing
Год: 2024
Страниц: 148
Формат: PDF
Размер: 112 MB
Язык: английский

Welcome to the 2024 edition of the Hacker's Manual! You hold in your hands 148 pages of Linux hacking tutorials, guides and features from the experts at Linux Format magazine - the home of open source software. Over 80 hacking tutorials to get your teeth into! Networking – dive into the protocols, build a network and analyse your traffic. Sysadmin – harness the power of Docker and learn time-saving terminal tricks. Hacking – get a full Linux OS on your phone and hack your distro for speed. Privacy – give The Man the slip and protect your privacy with our help.

Название: AI In Digital Marketing
Автор: Maria Johnsen
Издательство: Mercury Learning and Information
Серия: MLI Generative AI Series
Год: 2024
Страниц: 539
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB

This book presents a comprehensive and innovative exploration of the role of Artificial Intelligence (AI) in the digital marketing arena. It begins with historical context and fundamental AI principles, and subsequently, details AI's applications across a spectrum of areas, including data analytics, content creation, customer targeting, Search Engine Optimization (SEO), Pay Per Click (PPC) advertising, Social Media Marketing (SMM), and Email Marketing. A distinctive feature lies in the author's extensive real-world experience, providing many useful business applications of AI. The book is designed for marketing professionals, business executives, educators, and students, and offers numerous examples and case studies. Machine Learning and Deep Learning represent two of the most captivating and transformative domains within contemporary technology. Over the past decade, these fields have undergone rapid and profound development, exerting a profound influence across various industries and fundamentally altering our approach to intricate problem-solving. In this chapter, we will delve into the fundamental principles, methodologies, and practical applications that underpin these disciplines. Furthermore, we will scrutinize the salient disparities between them and explore their consequential real-world ramifications. Machine Learning algorithms constitute mathematical models and techniques designed to assimilate knowledge from data. Prominent Machine Learning algorithms include linear regression, decision trees, support vector machines, and k-nearest neighbors. Machine Learning models acquire knowledge through exposure to substantial volumes of labeled data. During the training process, the model adapts its internal parameters to minimize the disparities between its predictions and the actual data. Developing custom Machine Learning models is an option for businesses that want to create highly tailored customer profiles. Python libraries like Scikit-learn and TensorFlow are often used for this purpose.

Название: Intelligent Optimization: Principles, Algorithms and Applications
Автор: Changhe Li, Shoufei Han, Sanyou Zeng
Издательство: Springer
Год: 2024
Страниц: 369
Язык: английский
Формат: pdf (true), epub
Размер: 34.6 MB

This textbook comprehensively explores the foundational principles, algorithms, and applications of intelligent optimization, making it an ideal resource for both undergraduate and postgraduate artificial intelligence courses. It remains equally valuable for active researchers and individuals engaged in self-study. Serving as a significant reference, it delves into advanced topics within the evolutionary computation field, including multi-objective optimization, dynamic optimization, constrained optimization, robust optimization, expensive optimization, and other pivotal scientific studies related to optimization. Firstly, the relationship between optimization and Machine Learning is discussed, and an example of Machine Learning task is given. Secondly, the mathematical formulation of an optimization problem is defined. Optimization problems are categorized into continuous optimization problems and discrete optimization problems. Finally, optimization algorithms are introduced and categorized into deterministic algorithms and probabilistic algorithms, where several terms regarding intelligent optimization are introduced. Designed to be approachable and inclusive, this textbook equips readers with the essential mathematical background necessary for understanding intelligent optimization. It employs an accessible writing style, complemented by extensive pseudo-code and diagrams that vividly illustrate the mechanisms, principles, and algorithms of optimization. With a focus on practicality, this textbook provides diverse real-world application examples spanning engineering, games, logistics, and other domains, enabling readers to confidently apply intelligent techniques to actual optimization problems. Recognizing the importance of hands-on experience, the textbook introduces the Open-source Framework for Evolutionary Computation platform (OFEC) as a user-friendly tool. This platform serves as a comprehensive toolkit for implementing, evaluating, visualizing, and benchmarking various optimization algorithms.