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 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. | |
Разместил: Ingvar16 1-01-2024, 06:44 | Комментарии: 0 | Подробнее
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 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. | |
Разместил: Ingvar16 31-12-2023, 18:41 | Комментарии: 0 | Подробнее
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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».
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Разместил: Chipa 31-12-2023, 16:06 | Комментарии: 0 | Подробнее
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 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. | |
Разместил: Ingvar16 31-12-2023, 08:49 | Комментарии: 0 | Подробнее
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 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. | |
Разместил: Ingvar16 30-12-2023, 22:02 | Комментарии: 0 | Подробнее
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 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. | |
Разместил: Ingvar16 30-12-2023, 19:47 | Комментарии: 0 | Подробнее
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 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. | |
Разместил: Ingvar16 30-12-2023, 18:45 | Комментарии: 0 | Подробнее
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 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. | |
Разместил: Ingvar16 30-12-2023, 08:55 | Комментарии: 0 | Подробнее
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 Название: Scripting Farming Simulator with Lua: Unlocking the Virtual Fields
Автор: Zander Brumbaugh, Manuel Leithner
Издательство: Apress
Год: 2024
Страниц: 343
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB
Create mods using the popular game Farming Simulator with Lua, a versatile scripting language that can run on various platforms and applications. This guide is best suited for programmers who want to learn how to use Lua to write scripts that can enhance and extend the gameplay experience. You'll start by reviewing the basics of programming in Lua and then move on to advanced topics, such as object-oriented programming, vehicle and placeable specializations, network synchronization, and creating custom user interfaces. With step-by-step instructions and detailed explanations, you'll see how to create scripts that modify game mechanics and add new features to the game. The practical examples and projects are ideal for providing hands-on experience with Lua scripting. Whether you are a creator looking to take your skills to the next level or a game developer interested in learning a new programming language, Scripting Farming Simulator with Lua is the ultimate guide to mastering Lua scripting. For creators who want to add new features to Farming Simulator; game developers who want to learn a new programming language for modding purposes, and anyone who wants to expand their knowledge of programming and scripting. | |
Разместил: Ingvar16 30-12-2023, 08:15 | Комментарии: 0 | Подробнее
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 Название: .NET Core For Machine Learning: Build Smart, Fast, And Reliable Solutions
Автор: Hayden Van Der Post, Mike Smith
Издательство: Reactive Publishing
Год: 2023
Страниц: 361
Язык: английский
Формат: pdf
Размер: 32.1 MB
.NET Core For Machine Learning" is an advanced guide designed for professionals seeking to enhance their expertise in Data Science and Machine Learning using the capabilities of .NET Core 3.1. This cutting-edge resource provides valuable insights and techniques to bolster analytical skills and leverage the robust features of .NET Core 3.1 in the field of Machine Learning. As you dive deeper into this comprehensive book, you will discover a treasure trove of sophisticated techniques to elevate your expertise. It is specifically crafted for readers who were captivated by the top-selling predecessors but are now craving more complex challenges and solutions. With each chapter, you are presented with a rich array of real-world projects, designed to push the boundaries of your knowledge and skills. Implement smart algorithms, build fast and reliable data processing pipelines, and create interactive Machine Learning models that can seamlessly scale to meet the demanding needs of today's data-driven industry. ".NET Core For Machine Learning" is not just a technical manual; it is your gateway to mastering the art of Machine Learning and transforming data into actionable knowledge. Whether you're looking to revolutionize your business strategy or innovate within your technical team, this book will prove an invaluable asset on your quest for excellence. Your journey towards becoming a Data Science maestro begins here. | |
Разместил: Ingvar16 30-12-2023, 07:45 | Комментарии: 0 | Подробнее
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