Название: High-Performance Algorithmic Trading Using AI: Strategies and Insights for Developing Cutting-Edge Trading Algorithms
Автор: Melick R. Baranasooriya
Издательство: BPB Publications
Год: 2024
Страниц: 358
Язык: английский
Формат: pdf, epub (true), mobi
Размер: 10.1 MB

"High-Performance Algorithmic Trading using AI" is a comprehensive guide designed to empower both beginners and experienced professionals in the finance industry. This book equips you with the knowledge and tools to build sophisticated, high-performance trading systems. It starts with basics like data preprocessing, feature engineering, and ML. Then, it moves to advanced topics, such as strategy development, backtesting, platform integration using Python for financial modeling, and the implementation of AI models on trading platforms. Each chapter is crafted to equip readers with actionable skills, ranging from extracting insights from vast datasets to developing and optimizing trading algorithms using Python's extensive libraries. It includes real-world case studies and advanced techniques like Deep Learning and reinforcement learning. The book wraps up with future trends, challenges, and opportunities in algorithmic trading.

Название: Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide
Автор: Van Thanh Tien Nguyen, Nhut T.M. Vo, Van Chinh Truong
Издательство: CRC Press
Год: 2025
Страниц: 361
Язык: английский
Формат: pdf (true)
Размер: 13.4 MB

As multicriteria decision-making (MCDM) continues to grow and evolve, Machine Learning (ML) techniques have become increasingly important in finding efficient and effective solutions to complex problems. This book is intended to guide researchers, practitioners, and students interested in the intersection of ML and MCDM for optimal design. Multi-Criteria Decision-Making and Optimum Design with Machine Learning: A Practical Guide is a comprehensive resource that bridges the gap between ML and MCDM. It offers a practical approach by demonstrating the application of ML and MCDM algorithms to real-world problems. Through case studies and examples, it showcases the effectiveness of these techniques in optimal design. The book also provides a comparative analysis of conventional MCDM algorithms and Machine Learning techniques, enabling readers to make informed decisions about their use in different scenarios. This book is designed for professionals, researchers, and practitioners in engineering, Computer Science, sustainability, and related fields. It is also a valuable resource for students and academics who wish to expand their knowledge of Machine Learning applications in multicriteria decision-making.

Название: Artificial Intelligence: Technical and Societal Advancements
Автор: Utku Kose, Mustafa Umut Demirezen
Издательство: CRC Press
Год: 2025
Страниц: 349
Язык: английский
Формат: pdf (true)
Размер: 36.8 MB

This book provides an examination of cutting-edge research and developments in the field of Artificial Intelligence (AI). It seeks to extend the view in both technical and societal evaluations to ensure a well-defined balance for societal outcomes. It explores hot topics such as Generative Artificial Intelligence (GenAI), Artificial Intelligence in law, education, and climate change. Artificial Intelligence: Technical and Societal Advancements seeks to bridge the gap between theory and practical applications of AI by giving readers insight into recent advancements. It offers readers a deep dive into the transformative power of AI for the present and future world. As Artificial Intelligence continues to revolutionize various sectors, the book discusses applications from healthcare to finance and from entertainment to industrial areas. It discusses the technical aspects of intelligent systems and the effects of these aspects on humans. To this point, this book considers technical advancements while discussing the societal pros and cons in terms of human-machine interaction in critical applications. The target readers of this book include academicians; researchers; experts; policymakers; educators; and B.S., M.S., and Ph.D. students in the context of target problem fields. It can be used accordingly as a reference source and even supportive material for Artificial Intelligence-oriented courses.

Название: Supply Chain Software Security: AI, IoT, and Application Security
Автор: Aamiruddin Syed
Издательство: Apress
Год: 2024
Страниц: 533
Язык: английский
Формат: pdf
Размер: 10.1 MB

Delve deep into the forefront of technological advancements shaping the future of supply chain safety and resilience. In an era where software supply chains are the backbone of global technology ecosystems, securing them against evolving threats has become mission critical. This book offers a comprehensive guide to understanding and implementing next-generation strategies that protect these intricate networks from most pressing risks. This book begins by laying the foundation of modern software supply chain security, exploring the shifting threat landscape and key technologies driving the future. Through real-world case studies and practical insights, learn how to build resilient supply chains equipped to defend against sophisticated attacks like dependency confusion, backdoor injection, and adversarial manipulation. Whether you're managing a global software operation or integrating DevSecOps into your CI/CD pipelines, this book offers actionable advice for fortifying your supply chain end-to-end. The target audience for a book would typically include professionals and individuals with an interest or involvement in cloud-native application development and DevOps practices.

Название: ASYNC Rust: Unleashing the Power of Fearless Concurrency
Автор: Maxwell Flitton, Caroline Morton
Издательство: O’Reilly Media, Inc.
Год: 2025
Страниц: 315
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Already popular among programmers for its memory safety and speed, the Rust programming language is also valuable for asynchrony. This practical book shows you how asynchronous Rust can help you solve problems that require multitasking. You'll learn how to apply async programming to solve problems with an async approach. You will also dive deeper into async runtimes, implementing your own ways in which async runtimes handle incoming tasks. Asynchronous programming in Rust, often referred to as Async Rust, is a powerful paradigm that allows developers to write concurrent code that is more efficient and scalable. Rust’s type system and ownership model provide the safety guarantees that we all love. However, mastering async Rust requires an understanding of specific concepts, such as futures, pinning, and executors. This book will guide you through these concepts, equipping you with the knowledge needed to apply async to your own projects and programs. This book is aimed at intermediate Rust developers who want to learn how to improve their applications and programs by using the range of asynchronous functionality available to them.

Название: The Data Science Handbook, 2nd Edition
Автор: Field Cady
Издательство: Wiley
Год: 2025
Страниц: 368
Язык: английский
Формат: True/Retail EPUB, PDF
Размер: 10.1 MB

Practical, accessible guide to becoming a data scientist, updated to include the latest advances in Data Science and related fields. Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline. Data Science is a quickly evolving field, and this 2nd edition has been updated to reflect the latest developments, including the revolution in AI that has come from Large Language Models and the growth of ML Engineering as its own discipline. I have also updated my treatment of Spark to cover its new DataFrame interface, and reduced the emphasis on Hadoop since it is on the decline. Other changes include a reduced emphasis on Bayesian networks (which have waned in popularity with the rise of Deep Learning), a switch from Python 2 to Python 3, and numerous improvements to the prose. The example code in this book is all in Python, except for a few domain‐specific languages such as SQL.

Название: 300+ Python Algorithms: Mastering the Art of Problem-Solving
Автор: Hernando Abella
Издательство: Aluna Publishing House
Год: 2024
Страниц: 399
Язык: английский
Формат: pdf, epub (true), azw3, mobi
Размер: 10.1 MB

This book provides an essential collection of algorithms designed to help you solve a wide range of programming problems. It's perfect for beginners looking to gain key knowledge and improve their skills efficiently. With clear and practical explanations, this book serves as an excellent guide for those just starting their programming journey, offering a solid foundation to help them progress quickly. Why you should study algorithms: Problem-Solving Skills: Algorithms help you approach and solve complex problems efficiently, which is essential in programming and real-life applications. Efficient Coding: Understanding algorithms allows you to write code that performs faster and uses fewer resources, leading to optimized software. Foundational Knowledge: Algorithms form the backbone of computer science, giving you a strong foundation for advanced topics like Artificial Intelligence, Machine Learning, and cryptography. Software Efficiency: By studying algorithms, you learn how toimprove the overall efficiency of the software you build, which can lead to better user experiences and lower operational costs.

Название: Machine Learning Evaluation: Towards Reliable and Responsible AI, 2nd Revised Edition
Автор: Nathalie Japkowicz, Zois Boukouvalas
Издательство: Cambridge University Press
Год: 2025
Страниц: 427
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB

As Machine Learning applications gain widespread adoption and integration in a variety of applications, including safety and mission-critical systems, the need for robust evaluation methods grows more urgent. This book compiles scattered information on the topic from research papers and blogs to provide a centralized resource that is accessible to students, practitioners, and researchers across the sciences. The book examines meaningful metrics for diverse types of learning paradigms and applications, unbiased estimation methods, rigorous statistical analysis, fair training sets, and meaningful explainability, all of which are essential to building robust and reliable Machine Learning products. In addition to standard classification, the book discusses unsupervised learning, regression, image segmentation, and anomaly detection. The book also covers topics such as industry-strength evaluation, fairness, and responsible AI. Implementations using Python and Scikit-learn are available on the book's website.

Название: Серия "Российский компьютерный бестселлер" в 12 книгах
Автор(ы): Васильев А.
Издательство: Москва
Год: 2017-2023
Страниц: 1000+
Формат: PDF
Размер: 271 Мб
Язык: русский

В серию "Российский компьютерный бестселлер" вошли самоучители Алексея Васильева, доктора физико-математических наук и автора более пятнадцати книг по программированию. Все издания основаны на уникальной авторской методике обучения, которая помогла тысячам читателей освоить языки C, C++, Java, jаvascript и другие.

Название: Атлас искусственного интеллекта: руководство для будущего
Автор: Кейт Кроуфорд
Издательство: АСТ
Серия: Программирование для всех
Год: 2023
Страниц: 321
Язык: русский
Формат: pdf, fb2, epub
Размер: 11.3 MB

Искусственный интеллект стал неотъемлемой частью современного мира, помогая людям в множестве сфер – от медицины до тяжелой промышленности. Оптимизация рабочих процессов, скорость выполнения, машинная точность в расчетах или креатив в творчестве – кажется, что ИИ стал совершенным инструментом для любой задачи. Кейт Кроуфорд – старший научный сотрудник Microsoft, профессор Калифорнийского университета – предлагает нам книгу-исследование, обращая наше внимание на темную сторону успеха и скрытые издержки искусственного интеллекта. При контролируемом машинном обучении инженеры предоставляют компьютеру маркированные обучающие данные. Затем в игру вступают два различных типа алгоритмов: обучающие и классифицирующие. Обучающий алгоритм – это алгоритм, который учится на помеченных данных; он сообщает классификатору, как лучше проанализировать связь между новыми входными данными и желаемым конечным результатом (или предсказанием). Например, он может определить: содержится ли на изображении лицо, является ли электронное письмо спамом. Существует множество видов моделей машинного обучения, включая нейронные сети, логистическую регрессию и деревья решений. Инженеры выбирают модель в зависимости от того, что они создают – будь то система распознавания лиц или средство определения настроений в социальных сетях, – а затем подбирают ее под свои вычислительные ресурсы.