Litgu.ru - Литературный Гуру

Название: Глубокое обучение для поисковых систем
Автор: Теофили Т.
Издательство: ДМK
Год: 2020
Cтраниц: 318
Формат: pdf (ocr)
Размер: 25 мб
Язык: русский

В книге рассказывается о том, как использовать глубокие нейронные сети для создания эффективных поисковых систем. Рассматривается несколько компонентов поисковой системы, дается представление о том, как они работают, и приводятся рекомендации по использованию нейронных сетей в разных контекстах поиска.

Название: Создание Windows-приложений в среде Delphi
Автор: Федотова С.В.
Издательство: СОЛОН-Пресс
Год: 2016
Cтраниц: 220
Формат: pdf
Размер: 32 мб
Язык: русский

Учебное пособие «Создание Windows-приложений в среде Delphi» предназначено для учащихся средних школ, студентов технических вузов, преподавателей информатики. Данный курс программирования предполагает последовательное изучение материала от простого к сложному. Читателю, впервые приступившему к изучению темы, предлагаются все необходимые сведения для понимания и соответствующей организации процесса программирования при создании Windows-приложения.

Название: Engineering Mathematics with MATLAB and Simulink
Автор: Farzin Asadi
Издательство: Springer
Год: 2025
Страниц: 496
Язык: английский
Формат: pdf (true)
Размер: 43.1 MB

This book summarizes the mathematics used by engineers, with an emphasis on developing practical skills and techniques for solving mathematical problems in forms typical of engineering. In addition to paper-and-pencil techniques, the book demonstrates how to solve engineering mathematics problems using state-of-the-art software packages. Simulink is a powerful graphical programming environment designed for modeling, simulating, and analyzing dynamical systems. It’s part of the MATLAB suite of tools, offering a user-friendly interface for creating block diagrams that represent system components and their interactions.

Название: Math for Data Science
Автор: Omar Hijab
Издательство: Springer
Год: 2025
Страниц: 588
Язык: английский
Формат: pdf (true)
Размер: 20.8 MB

Math for Data Science presents the mathematical foundations necessary for studying and working in Data Science. The book is suitable for courses in applied mathematics, business analytics, Computer Science, Data Science, and engineering. The text covers the portions of linear algebra, calculus, probability, and statistics prerequisite to Data Science. The highlight of the book is the Machine Learning chapter, where the results of the previous chapters are applied to neural network training and stochastic gradient descent. Also included in this last chapter are advanced topics such as accelerated gradient descent and logistic regression trainability. Clear examples are supported with detailed figures and Python code; Jupyter notebooks and supporting files are available on the author's website. More than 380 exercises and nine detailed appendices covering background elementary material are provided to aid understanding. The standard Python data science packages are used, and a Python index lists the functions used in the text.

Название: Mathematical Foundations Guide to Neural Networks: CNNs, RNNs, LSTMs, Autoencoders, Attention Mechanisms, and More
Автор: Alec Stovari
Издательство: Independently published
Серия: Python Fundamentals
Год: 2025
Страниц: 179
Язык: английский
Формат: pdf
Размер: 10.1 MB

With clear explanations and detailed insights, in 170+ pages, you will learn the inner workings of backpropagation, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory networks (LSTMs). The book also dives into advanced techniques such as dropout, autoencoders, and attention layers that are transforming the AI landscape. Dive deep into the theory behind each model, understand their applications, and master the mathematics that power modern Machine Learning. Key Topics Covered: - The theoretical foundations of Neural Networks; - Backpropagation and optimization techniques; - Convolutional Neural Networks (CNNs) for image recognition and more; - Recurrent Neural Networks (RNNs) and their sequential data processing power; - Attention mechanisms and transformer models revolutionizing NLP; - Advanced Deep Learning architectures and real-world applications; - Mathematical principles behind Deep Learning algorithms. This book serves as both an academic reference and a practical guide.

Название: Architecting Go Applications: A Clean Approach to Building Scalable Gin Web Services
Автор: Aarav Joshi
Издательство: 101 Books
Год: 2025
Страниц: 1864
Язык: английский
Формат: epub (true)
Размер: 21.6 MB

This comprehensive guide takes developers through the journey of designing and building robust web applications using Go and the Gin framework. The book bridges theoretical clean architecture principles with practical implementation, providing a blueprint for creating maintainable, scalable, and high-performance web services. Starting with foundational concepts of clean architecture and Go programming paradigms, readers will progressively learn how to structure applications using SOLID principles and domain-driven design. The book offers detailed guidance on implementing clean architecture layers, building RESTful and GraphQL APIs, and establishing effective data access patterns. Advanced topics include microservice architecture, testing strategies across all layers, deployment pipelines, and performance optimization techniques. Real-world case studies demonstrate how to apply these principles to various application types, from e-commerce platforms to content management systems.

Название: Embodied Multi-agent Systems: Perception, Action and Learning
Автор: Huaping Liu, Xinzhu Liu, Kangyao Huang, Di Guo
Издательство: Springer
Серия: Machine Learning: Foundations, Methodologies, and Applications
Год: 2025
Страниц: 246
Язык: английский
Формат: pdf (true)
Размер: 19.7 MB

In recent years, embodied multi-agent systems, including multi-robots, have emerged as essential solution for demanding tasks such as search and rescue, environmental monitoring, and space exploration. Effective collaboration among these agents is crucial but presents significant challenges due to differences in morphology and capabilities, especially in heterogenous systems. While existing books address collaboration control, perception, and learning, there is a gap in focusing on active perception and interactive learning for embodied multi-agent systems. This book aims to bridge this gap by establishing a unified framework for perception and learning in embodied multi-agent systems. It presents and discusses the perception-action-learning loop, offering systematic solutions for various types of agents—homogeneous, heterogeneous, and ad hoc. Beyond the popular Reinforcement Learning techniques, the book provides insights into using fundamental models to tackle complex collaboration problems. By interchangeably utilizing constrained optimization, Reinforcement Learning, and fundamental models, this book offers a comprehensive toolkit for solving different types of embodied multi-agent problems. Readers will gain an understanding of the advantages and disadvantages of each method for various tasks. This book is suitable as a reference book for graduate students with a basic knowledge of Machine Learning, as well as for professional researchers interested in robotics and embodied intelligence.

Название: Generative AI for Cloud Solutions: Building end-to-end generative AI stacks with cloud services and model orchestration pipelines
Автор: Sireesha Muppala, Randy DeFauw, Sina Sojoodi
Издательство: BPB Publications
Год: 2025
Страниц: 316
Язык: английский
Формат: epub (true)
Размер: 15.2 MB

Generative AI is transforming every industry, with applications ranging from creative content generation, simple chatbots, to entirely new ways of engaging with consumers. But there is as much uncertainty as buzz—understanding how to use this technology securely and responsibly, and recognizing what the pitfalls are. In this book, we will put together a complete picture of Generative AI development on modern cloud platforms, covering all stages of building and operating a production-grade solution with consideration for performance, security, governance, and responsibility. Conceptual discussions will be accompanied by functional examples, using working code on Amazon Web Services (AWS) cloud to demonstrate key concepts. We will explore the full lifecycle, from initial model selection and fine-tuning to production deployment, monitoring, and ongoing operation. Key aspects include prompt engineering, data integration techniques, observability, the shared responsibility model, and the full solution lifecycle from design to operation. Additionally, we will discuss recommendations for prioritizing a Generative AI roadmap for organizations and emerging trends in the field.

Название: Разгон и оптимизация компьютера на 100%
Автор: Зозуля Ю.Н.
Издательство: Питер
Год: 2010
Страниц: 320
Формат: PDF
Размер: 13 Мб
Язык: русский

Вы хотите, чтобы ваш компьютер работал быстро и надежно? Значит, перед вами стоит выбор: либо потратить огромные деньги на приобретение самого мощного и современного «железа», либо купить эту книгу и узнать о простых и доступных способах разгона и оптимизации ПК. Она откроет вам секреты тонкой настройки и повышения производительности компьютера, которые известны только профессионалам.

Название: Infrastructure as Code with Azure Bicep: Infrastructure development through Bicep modules, template specs, and DevOps automation pipelines
Автор: Adora Nwodo
Издательство: BPB Publications
Год: 2025
Страниц: 268
Язык: английский
Формат: pdf, epub (true)
Размер: 11.2 MB

Azure Bicep is a powerful language for deploying Azure resources declaratively, providing a simpler and more efficient alternative to JSON for creating Azure Resource Manager templates. It streamlines the process of defining, provisioning, and managing cloud infrastructure, making Infrastructure as Code (IaC) more accessible and effective. This book provides a comprehensive guide to Azure Bicep, starting with an introduction to IaC, Bicep's core concepts, and a comparative analysis with ARM templates. It covers installation, setup, fundamental syntax, and practical template creation and deployment. Readers will learn to utilize variables, parameters, expressions, conditions, and loops for dynamic configurations. The book explores modularity through Bicep modules and template specs, as well as compilation and decompilation processes. Automation is emphasized with GitHub Actions and Azure DevOps CI/CD pipelines, including monitoring and troubleshooting. Advanced topics include scaling Bicep for large projects, secret management, cross-subscription deployments, and Policy as Code (PaC). By the end of the book, you will have the tools and knowledge to implement IaC principles in your workflow, accelerating the development of reliable and scalable Azure cloud applications.