Название: Advancing Edge Artificial Intelligence: System Contexts
Автор: Ovidiu Vermesan, Dave Marples
Издательство: River Publishers
Серия: River Publishers Series in Communications and Networking
Год: 2023
Страниц: 260
Язык: английский
Формат: pdf (true)
Размер: 14.6 MB

The intersection of Artificial Intelligence (AI), the Internet of Things (IoT) and Edge Computing has kindled the edge AI revolution that promises to redefine how we perceive and interact with the physical world through intelligent devices. Edge AI moves intelligence from the network centre to the devices at its edge, entrusting these endpoints to analyse data locally, make decisions, and provide real-time responses. Recent advances in power-efficient high-performance embedded silicon make edge AI a viable proposition, albeit one requiring new distributed architectures and novel design concepts. Moving decision-making closer to the edge makes responses faster and systems more reliable, while the constant pressure to reduce network bandwidth demand and the need to contain spiralling data storage and operations costs help justify the engineering investment necessary to embrace this new paradigm. Further, moving to decentralised operation opens the door to a multitude of novel applications, covering immersive technologies and autonomous systems across fields as diverse as healthcare and industrial automation, personal assistance and prognostics, surgery, and process control. In the best tradition of systems engineering, the first stage of this transition process is understanding the application domain for edge AI deployment, the "system context". Federated Learning (FL) has emerged as a promising technique that enables privacy-preserving development of ML models on low-energy Edge devices. FL is a distributed approach that enables learning from data belonging to multiple participants, without compromising privacy since user data are never directly shared.

Название: Introduction to Compiler Design 3rd Edition
Автор: Torben Ægidius Mogensen
Издательство: Springer
Серия: Undergraduate Topics in Computer Science
Год: 2024
Страниц: 303
Язык: английский
Формат: pdf (true), epub
Размер: 23.6 MB

In order to reduce the complexity of designing and building computers, nearly all of these are made to execute relatively simple commands (but do so very quickly). A program for a computer must be built by combining these very simple commands into a program in what is called machine language. Since this is a tedious and error-prone process, most programming is done using a high-level programming language. This language can be very different from the machine language that the computer can execute, so some means of bridging the gap is required. This is where the compiler comes in. A compiler translates (or compiles) a program written in a high-level programming language that is suitable for human programmers into the low-level machine language that is required by computers. During this process, the compiler will also attempt to detect and report obvious programmer mistakes. Using a high-level language for programming has a large impact on how fast programs can be developed. The third edition of this textbook has been fully revised and adds material about the SSA form, polymorphism, garbage collection, and pattern matching. It presents techniques for making realistic compilers for simple to intermediate-complexity programming languages. The techniques presented in the book are close to those used in professional compilers, albeit in places slightly simplified for presentation purposes. "Further reading" sections point to material about the full versions of the techniques. All phases required for translating a high-level language to symbolic machine language are covered, and some techniques for optimising code are presented. The intermediate language is translated to assembly language (a textual representation of machine code) for a specific machine architecture. The assembly language code is translated into binary representation and addresses of variables, functions, etc., are determined.

Название: Quantitative Economics with Python
Автор: Thomas J. Sargent, John Stachurski
Издательство: QuantEcon
Год: May 7, 2020
Страниц: 943
Язык: английский
Формат: pdf (true)
Размер: 11.9 MB

This book presents a set of lectures on Python programming for economics and finance. The lecture describes important ideas in economics that use the mathematics of geometric series. Linear algebra is one of the most useful branches of applied mathematics for economists to invest in. For example, many applied problems in economics and finance require the solution of a linear system of equations. In this lecture we will cover the basics of linear and matrix algebra, treating both theory and computation. We admit some overlap with this lecture, where operations on NumPy arrays were first explained. Note that this lecture is more theoretical than most, and contains background material that will be used in applications as we go along. In an earlier lecture on Pandas, we looked at working with simple data sets. Econometricians often need to work with more complex data sets, such as panels. Common tasks include: - Importing data, cleaning it and reshaping it across several axes. - Selecting a time series or cross-section from a panel. - Grouping and summarizing data. Pandas (derived from ‘panel’ and ‘data’) contains powerful and easy-to-use tools for solving exactly these kinds of problems.

Название: Mastering C++: A Comprehensive Beginner's Guide to Programming
Автор: Chloe Annable
Издательство: Independently published
Год: 2023
Страниц: 179
Язык: английский
Формат: epub (true)
Размер: 10.1 MB

Are you dealing with massive data volumes at work or in your hobbies? Need a powerful, reliable programming language to handle such demands? Look no further than this book! Packed with essential design and development insights, it swiftly introduces you to C++ concepts, enabling you to craft programs tailored to your specific needs. C++ remains a pivotal language due to its adaptability and robustness in modern contexts. But where does a newcomer begin? This guide jumpstarts your journey into programming, catering to complex environments and addressing your diverse needs. It lays a solid foundation for further exploration and knowledge expansion.

Название: Игровая разработка без боли и кранчей. Как выжить в игровой индустрии и сохранить вдохновение
Автор: Ричард Лемаршан
Издательство: Бомбора
Год: 2023
Формат: PDF/fb2/epub
Страниц: 481
Размер: 31 Mb
Язык: Русский

Перед вами книга, написанная легендарным разработчиком Ричардом Лемаршаном, которая расскажет, как соединить творческие аспекты гейм-дизайна с проверенными методами эффективного управления проектами. В ней описываются четыре этапа создания проекта: разработка идеи, подготовка к производству, производство и постпродакшн. Эта книга научит вас, как провести проект цифровой игры от начала до конца – от создания концепта и проектирования до сборки, тестирования и релиза – и избежать при этом неконтролируемого переутомления, известного среди разработчиков как «кранч».

Название: Computer Vision: Three-dimensional Reconstruction Techniques
Автор: Andrea Fusiello
Издательство: Springer
Год: 2024
Страниц: 348
Язык: английский
Формат: pdf (true), epub
Размер: 40.4 MB

From facial recognition to self-driving cars, the applications of computer vision are vast and ever-expanding. Geometry plays a fundamental role in this discipline, providing the necessary mathematical framework to understand the underlying principles of how we perceive and interpret visual information in the world around us. This text explores the theories and computational techniques used to determine the geometric properties of solid objects through images. It covers the basic concepts and provides the necessary mathematical background for more advanced studies. The book is divided into clear and concise chapters covering a wide range of topics including image formation, camera models, feature detection and 3D reconstruction. Each chapter includes detailed explanations of the theory as well as practical examples to help the reader understand and apply the concepts presented. Deep Learning has brought undeniable successes and some breakthroughs in image recognition and scene description. It is nevertheless true that geometric Computer Vision remains a fundamental field. Given the impressive state-of-the-art and the rapid pace of progress in Deep Learning, it would be of course risky to rule out the possibility that the solution to many geometric vision problems, for instance reconstructing 3D structure from multiple images, can be learned from millions of examples. Yet we believe that a principled, approach that obtains the geometric structure of what we see through applied mathematics provides more insight. We would also go as far as suggesting that, in the end, such an approach can be even more fun to study and implement. The book has been written with the intention of being used as a primary resource for students on university courses in Computer Vision, particularly final year undergraduate or postgraduate Computer Science or engineering courses. To aid the reader in implementation, most of the methods discussed in the book are accompanied by a Matlab listing and the sources are available on Github.

Название: Handbook of Face Recognition: The Deep Neural Network Approach, 3rd Edition
Автор: Stan Z. Li, Anil K. Jain, Jiankang Deng
Издательство: Springer
Год: 2024
Страниц: 473
Язык: английский
Формат: pdf (true)
Размер: 12.7 MB

Over the past decade, Deep Learning has emerged as a powerful tool for solving a wide range of complex problems in Computer Vision, speech recognition, and Natural Language Processing (NLP). One area where Deep Learning has shown particularly promising results is in face recognition. This highly anticipated new edition provides a comprehensive account of face recognition research and technology, spanning the full range of topics needed for designing operational recognition systems. After a thorough introduction, each subsequent chapter focuses on a specific topic, reviewing background information, up-to-date techniques, and recent results, as well as offering challenges and future directions. Face recognition is a critical technology with applications in security, surveillance, biometrics, and human-computer interaction. Deep learning-based approaches have achieved state-of-the-art performance in face recognition tasks, enabling accurate and efficient recognition of faces in a variety of settings. This handbook brings together some of the leading experts in the field of deep learning-based face recognition to provide a comprehensive overview of the current state of the art. This book serves as an all-encompassing resource, providing theoretical underpinnings, algorithms, and implementations to guide students, researchers, and practitioners across all aspects of face recognition. In addition to showcasing the most recent advancements in methods and algorithms, the book also supplies code and data to facilitate hands-on learning and the creation of reproducible face recognition algorithms and systems (Appendix) through Deep Learning programming. The code and data will be accessible on GitHub and will be updated regularly to keep the materials up to date.

Название: The Challenge to AI: Consciousness and Ecological General Intelligence
Автор: Stephen E. Robbins
Издательство: Mercury Learning and Information
Серия: MLI Generative AI Series
Год: 2024
Страниц: 365
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB

This book invites readers to explore the intricate world where Artificial Intelligence (AI), consciousness, and human cognition intersect. This groundbreaking book considers the profound differences between man and machine, challenging existing notions in AI and cognitive science. It argues that the key to understanding intelligence lies not in software, but in the hardware of our brain – a complex biochemical system far removed from current AI architectures. Through a deep examination of time, perception, language, and the nature of thought, the book presents a compelling case for the indispensability of biology and consciousness in cognition. To achieve this, to engineer this, will indeed be a challenge for AI. Artificial general intelligence (AGI), is the gleam in the eye of the AI community. This gleam was there “at the beginning,” the beginning being in the 1950s. However, in the author’s experience (circa 1972) the computer metaphor of mind swept into a then rather sleepy cognitive psychology just barely emerging from the grips of Skinner’s rats, a field that, given this injection of the computational framework, ultimately became cognitive science. This beginning was with the publication of Newell and Simon’s Human Problem Solving, a work dedicated to showing that a fundamental algorithm, means–ends analysis, could be applied to tasks like theorem proving, cryptarithmetic problems, even chess. The generative pretrained transformers (GPTs) of generative AI, we shall see, choose a different route, in one way doing an end-around for the entire problem, in another way, simply following the same approach, but essentially relying on a form of a premade solution to the problem.

Название: The Creation of a Conscious Machine: The Quest for Artificial Intelligence, 2nd Edition
Автор: Jean E. Tardy
Издательство: Mercury Learning and Information
Серия: MLI Generative AI Series
Год: 2024
Страниц: 327
Язык: английский
Формат: pdf, epub (true)
Размер: 10.1 MB

This book presents a groundbreaking journey into the world of Generative AI technology and offers an in-depth look at the prospect of AI achieving consciousness. The book navigates through various historical and modern perspectives on AI, from ancient myths to the Turing Test to the latest in technological advancements. It covers the theoretical and practical aspects of creating a conscious AI, including the specifications for synthetic consciousness and the integration of AI with human cognition. The book questions whether generative AI can meet the traditional criteria of consciousness and how this might be realized. In 2011, I published the first version of The Creation of a Conscious Machine, tracing the evolution of the quest for AI from antiquity to the present, culminating in a definition of artificial consciousness expressed in terms of clearly defined and achievable specifications. In 2015, I published The Meca Sapiens Blueprint, a complete system architecture to implement consciousness in autonomous agents. Recently, a new type of AI system, based on Generative AI technology, was officially released. These systems are very different from autonomous agents. They are deep learning systems that integrate large amounts of existing documentation to produce conversational output that can be stunningly convincing. This raises a question: Can generative AI meet the Meca Sapiens’ specifications of consciousness, and, if so, how? In this new, expanded, version of The Creation of a Conscious Machine, we describe how the core conditions of consciousness, initially intended for synthetic agents, can also be applied to generative AI, and we outline how this can be achieved. The original ­content is also updated and clarified in light of recent ­developments in AI.

Название: Quantum Machine Learning: Thinking and Exploration in Neural Network Models for Quantum Science and Quantum Computing
Автор: Claudio Conti
Издательство: Springer
Год: 2024
Страниц: 393
Язык: английский
Формат: pdf (true)
Размер: 14.6 MB

This book presents a new way of thinking about quantum mechanics and Machine Learning by merging the two. Quantum mechanics and Machine Learning may seem theoretically disparate, but their link becomes clear through the density matrix operator which can be readily approximated by neural network models, permitting a formulation of quantum physics in which physical observables can be computed via neural networks. As well as demonstrating the natural affinity of quantum physics and Machine Learning, this viewpoint opens rich possibilities in terms of computation, efficient hardware, and scalability. One can also obtain trainable models to optimize applications and fine-tune theories, such as approximation of the ground state in many body systems, and boosting quantum circuits’ performance. The book begins with the introduction of programming tools and basic concepts of Machine Learning, with necessary background material from quantum mechanics and quantum information also provided. This enables the basic building blocks, neural network models for vacuum states, to be introduced. The highlights that follow include: non-classical state representations, with squeezers and beam splitters used to implement the primary layers for quantum computing; boson sampling with neural network models; an overview of available quantum computing platforms, their models, and their programming; and neural network models as a variational ansatz for many-body Hamiltonian ground states with applications to Ising machines and solitons. The book emphasizes coding, with many open source examples in Python and TensorFlow, while MATLAB and Mathematica routines clarify and validate proofs.