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 Название: Deep Learning Concepts in Operations Research
Автор: Biswadip Basu Mallik, Gunjan Mukherjee, Rahul Kar, Aryan Chaudhary
Издательство: CRC Press
Серия: Advances in Computational Collective Intelligence
Год: 2025
Страниц: 277
Язык: английский
Формат: pdf (true)
Размер: 29.2 MB
The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of the Machine Learning (ML) paradigm in diversified fields of technology. Deep Learning Concepts in Operations Research looks at the concepts that are the foundation of this model-based approach. Apart from the classification process, the Machine Learning model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of Artificial Intelligence (AI) and ML as well. Deep Learning is derived from traditional neural networks, but is much more efficient than its predecessors. Also, Deep Learning uses both transformations and graphs to create multi-?layered learning models. Recently developed DL techniques include sound and discourse handling, visual information preparation, common dialect handling (NLP), and the like. Operations research is the branch of mathematics for performing many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by Artificial Intelligence and Machine Learning. Operations research has many beneficial aspects for decision making. Discussing how a proper decision depends on several factors, the book examines how AI and ML can be used to model equations and define constraints to solve problems and discover proper and valid solutions more easily. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. | |
Разместил: Ingvar16 31-07-2024, 16:49 | Комментарии: 0 | Подробнее
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 Название: Business Analytics with R and Python
Автор: David L. Olson, Desheng Dash Wu, Cuicui Luo, Majid Nabavi
Издательство: Springer
Серия: AI for Risks
Год: 2024
Страниц: 201
Язык: английский
Формат: pdf (true), epub
Размер: 42.5 MB
This book provides an overview of data mining methods in the field of business. Business management faces challenges in serving customers in better ways, in identifying risks, and analyzing the impact of decisions. Of the three types of analytic tools, descriptive analytics focuses on what has happened and predictive analytics extends statistical and/or Artificial Intelligence (AI) to provide forecasting capability. R is a command line software. R Studio, a Graphical User Interface (GUI) and integrated programming environment developed for R, provides a user-friendly programming environment by making screen menus for packages, functions, and files, surfing and saving utilities, and concurrent access to multiple scripts, files, and directories. R packages provide pre-programmed functions for a variety of analytical processes. Python is also a command line programming language. Python base is a limited library, available to download with a standard command line editor. Python is available for various operating systems like Windows, macOS, and Linux. Chapter 1 provides an overview of business management problems. Chapter 2 describes how analytics and knowledge management have been used to better cope with these problems. Chapter 3 describes initial data visualization tools. Chapter 4 describes association rules and software support. Chapter 5 describes cluster analysis with software demonstration. Chapter 6 discusses time series analysis with software demonstration. Chapter 7 describes predictive classification data mining tools. Applications of the context of management are presented in Chapter 8. Chapter 9 covers prescriptive modeling in business and applications of Artificial Intelligence. | |
Разместил: Ingvar16 31-07-2024, 16:06 | Комментарии: 0 | Подробнее
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 Название: Prototyping Python Dashboards for Scientists and Engineers: Build and Deploy a Complete Dashboard with Python
Автор: Padraig Houlahan
Издательство: Apress
Год: 2024
Страниц: 220
Язык: английский
Формат: pdf (true), epub (true)
Размер: 22.2 MB
Create interactive and data-driven dashboards using Python. This hands-on guide is a practical resource for those (with modest programming skills) in scientific and engineering fields looking to leverage Python's power for data visualization and analysis in a user-friendly dashboard format. You’ll begin by gaining a fundamental understanding of Python programming, including data types, lists, dictionaries, and data structures. The book then delves into the world of reactive programming with Plotly and Dash, offering a hands-on approach to building interactive web-based dashboards. Next, you’ll see how to work with online data, how to scrape and clean datasets, and keep files up-to-date. The book also guides you through planning a dashboard prototype, outlining project tasks, trends, forecasts, spectra, and other design considerations. It concludes with a discussion of how the dashboard can be used for data visualization of real data, explaining the usefulness of tools such as spectra. For scientists, engineers, students, programmers, and data enthusiasts who aspire to harness Python's potential for data visualization and analysis through the creation of interactive dashboards. | |
Разместил: Ingvar16 31-07-2024, 06:05 | Комментарии: 0 | Подробнее
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Название: Машинное обучение. Портфолио реальных проектов
Автор(ы): Григорьев Алексей
Издательство: Питер
Год: 2023
Страниц: 496
Формат: PDF
Размер: 12 Мб
Язык: русский
Изучите ключевые концепции машинного обучения‚ работая над реальными проектами! Машинное обучение - то, что поможет вам в анализе поведения клиентов, прогнозировании тенденций движения цен, оценке рисков и многом другом. Чтобы освоить машинное обучение, вам нужны отличные примеры, четкие объяснения и много практики. В книге все это есть!
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Разместил: MIHAIL62 30-07-2024, 21:08 | Комментарии: 0 | Подробнее
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 Название: How To Python
Автор: Ben Good
Издательство: Independently published
Год: 2024
Страниц: 104
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB
Embark on your coding journey with Ben Good’s transformative book, "How To Python". Ideal for novices and those looking to refresh their skills, this guide breaks down Python programming into digestible, engaging lessons packed with real-world examples. Each section builds on the previous, from simple scripts to complex algorithms, ensuring a solid foundation in Python’s dynamic capabilities. Ben Good empowers you to craft, debug, and deploy your applications, pushing the boundaries of what you can achieve with code. Step into the realm of programming with confidence and creativity with this essential Python manual! “How to Python” - a comprehensive guide designed to illuminate the path for those embarking on their journey through the dynamic and expansive world of Python programming. Whether you’re a complete novice looking to make your first foray into coding, or an experienced developer seeking to deepen your expertise in Python, this book aims to equip you with the knowledge and tools necessary to excel in this versatile language. Python, known for its simplicity yet powerful capabilities, sits at the forefront of innovation in software development. It serves as the backbone of numerous modern technologies, including web development, data analysis, Artificial Intelligence, and more. This versatility is one of the reasons Python has become one of the most popular programming languages in the world today. In writing this book, we’ve meticulously crafted each chapter to build upon the last, ensuring a structured learning experience that not only teaches you how to code in Python but also how to think like a Python programmer. From setting up your development environment and writing your first Python script to delving into advanced topics like Machine Learning and cloud integration, “How to Python” covers a broad spectrum of practical and engaging content. | |
Разместил: Ingvar16 30-07-2024, 15:16 | Комментарии: 0 | Подробнее
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 Название: Ada Programming: Reliable, Strongly-Typed Systems Programming
Автор: Theophilus Edet
Издательство: CompreQuest Books
Год: 2024
Страниц: 769
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB
Ada Programming: Reliable, Strongly-Typed Systems Programming offers an in-depth exploration of Ada, a language renowned for its reliability, robustness, and versatility in systems programming. Whether you're a seasoned developer or just beginning your journey, this comprehensive guide will help you master Ada and its unique features through a comprehensive set of essential constructs of Variables, Functions, Conditions, Collections, Loops, Comments, Enums, Classes, Accessors, and Scope, as well as the Ada's strong support for implementations of Concurrent Programming, Object-Oriented Programming (OOP), Procedural Programming, Security-Oriented Programming (SOP), and Structured Programming. Ada stands out in the programming world due to its strong typing, modularity, and exceptional error-checking capabilities. Designed for reliability and efficiency, Ada is the go-to language for critical systems where safety and security are paramount. This book delves into these essential features, providing a solid foundation for developing reliable and maintainable software systems. Learn about Ada’s powerful type system, modular programming capabilities, and robust error-handling mechanisms, all of which are crucial for creating software that is both reliable and maintainable. The journey begins with an introduction to the basic constructs of Ada, providing readers with a solid foundation in the language's syntax and semantics. We explore fundamental concepts such as data types, control structures, and subprograms, emphasizing Ada’s strict type checking and error prevention mechanisms. Through clear explanations and practical examples, readers will gain confidence in writing correct and efficient Ada code from the outset. Building on this foundation, the book transitions into more advanced topics, including object-oriented programming, generic programming, and tasking. These chapters highlight Ada’s unique approach to these paradigms, demonstrating how its features can be used to create modular, reusable, and concurrent systems. By understanding these advanced concepts, readers will be equipped to tackle complex software challenges with Ada’s robust toolkit. | |
Разместил: Ingvar16 30-07-2024, 13:06 | Комментарии: 0 | Подробнее
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 Название: Artificial Intelligence: A Guide for Everyone
Автор: Arshad Khan
Издательство: Springer
Год: 2024
Страниц: 278
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB
Enterprises, as well as individuals, are racing to reap the benefits of Artificial Intelligence (AI). However, in most cases, they are doing so without understanding the technology or its implications and risks, which can be significant. Artificial Intelligence: A Guide for Everyone is a step in addressing that gap by providing information that readers can easily understand at every level. This book aims to provide useful information to those planning, developing, or using AI, which has the potential to transform industries and shape the future. Artificial Intelligence: A Guide for Everyone encompasses theoretical as well as practical aspects of AI across various industries and applications. It demystifies AI by explaining, in a language that non-techies can follow, its history, different types, differentiating technologies, and various aspects of implementation. It explains the connection between AI theory and real-world application across diverse industries and how it fuels innovation. Whether you are a simple business user, a professional, a tech enthusiast, a manager, an executive, or an aspiring AI developer, this guide is your roadmap to navigate the intricacies of AI. The topics covered in this book are comprehensive and diverse. This guide explains the prerequisites for building, developing, and deploying an AI system. It discusses its key techniques, technologies, and components, such as Machine Learning, natural language processing, and Deep Learning. It explains the process of constructing an AI system, offering insights into problem definition, data preprocessing, model development, evaluation, testing, deployment, and monitoring. In consideration of practical resource limitations and constraints, pre-built, ready-to-use AI solutions, which democratize AI, are discussed. The journey concludes by categorizing AI based on intelligence levels and functionality, providing a comprehensive understanding of Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), Artificial Super Intelligence (ASI), and other categories. | |
Разместил: Ingvar16 30-07-2024, 11:57 | Комментарии: 0 | Подробнее
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 Название: Geophysical Data Analysis and Inverse Theory with MATLAB and Python, 5th Edition
Автор: William Menke
Издательство: Academic Press, Elsevier
Год: 2024
Страниц: 344
Язык: английский
Формат: pdf (true)
Размер: 29.7 MB
Geophysical Data Analysis and Inverse Theory with MATLAB or Python, Fifth Edition is a revised and expanded introduction to inverse theory and tomography as it is practiced by geophysicists. The book demonstrates the methods needed to analyze a broad spectrum of geophysical datasets, with special attention given to those methods that generate images of the earth. Data analysis can be a mathematically complex activity, but the treatment in this volume is carefully designed to emphasize those mathematical techniques that readers will find the most familiar and to systematically introduce less-familiar ones. A series of "crib sheets" offer step-by-step summaries of methods presented. Utilizing problems and case studies, along with MATLAB and Python computer code and summaries of methods, the book provides professional geophysicists, students, data scientists and engineers in geophysics with the tools necessary to understand and apply mathematical techniques and inverse theory. MATLAB and Python scripts are used throughout the book as a means of communicating how the formulas of inverse theory can be used in computer-based data processing scenarios. MATLAB is a commercial software product of The MathWorks, Inc. Python and its many add-ons, such as the NumPy linear algebra package, the SciPy scientific function package, and the Jupyter Notebook/JupyterLab environment, are freeware that offer an alternative to MATLAB and other commercial products with license fees. Both are widely used in university settings as an environment for scientific computing. | |
Разместил: Ingvar16 30-07-2024, 03:28 | Комментарии: 0 | Подробнее
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 Название: Collaborative Filtering: Recommender Systems
Автор: Angshul Majumdar
Издательство: CRC Press
Год: 2025
Страниц: 142
Язык: английский
Формат: pdf (true)
Размер: 14.2 MB
This book dives into the inner workings of recommender systems, those ubiquitous technologies that shape our online experiences. From Netflix show suggestions to personalized product recommendations on Amazon or the endless stream of curated YouTube videos, these systems power the choices we see every day. Collaborative filtering reigns supreme as the dominant approach behind recommender systems. This book offers a comprehensive exploration of this topic, starting with memory-based techniques. These methods, known for their ease of understanding and implementation, provide a solid foundation for understanding collaborative filtering. As you progress, you’ll delve into latent factor models, the abstract and mathematical engines driving modern recommender systems. The journey continues with exploring the concepts of metadata and diversity. You’ll discover how metadata, the additional information gathered by the system, can be harnessed to refine recommendations. Additionally, the book delves into techniques for promoting diversity, ensuring a well-balanced selection of recommendations. Finally, the book concludes with a discussion of cutting-edge Deep Learning models used in recommender systems. Recommender systems, powered by sophisticated algorithms and Machine Learning techniques, analyze user behavior, preferences, and past consumption patterns to identify patterns and predict future choices. These systems act as digital surrogates for the trusted friends, family, and experts who traditionally guided consumer decisions in the pre-internet era. The book assumes a basic understanding of linear algebra and optimization but requires no prior knowledge of Machine Learning or programming. | |
Разместил: Ingvar16 30-07-2024, 02:50 | Комментарии: 0 | Подробнее
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 Название: Introducing Microsoft Copilot for Managers: Enhance Your Team's Productivity and Creativity with Generative AI-Powered Assistant
Автор: Adeel Khan
Издательство: Apress
Год: 2024
Страниц: 696
Язык: английский
Формат: pdf
Размер: 32.1 MB
Embark on an exhilarating journey through the digital landscape with Microsoft Copilots, a suite of AI-powered tools that help you create, edit, and optimize content, code, and data across various Microsoft platforms. This book will be your indispensable guide to harnessing the power of generative AI within the Microsoft ecosystem, introducing you to various copilots and their impact on your business processes. The book will guide you as a manager to leverage Microsoft Copilot to improve your personal productivity, your team's productivity, and achieve your business goals with success. This book is organized into five parts. The first part introduces you to Microsoft Copilot and its features and benefits. In the second part, you will get an overview of the integration of Microsoft Copilot with M365 tools. The third part teaches you about the maker copilot in Power Platform services followed by the business expert copilot for Sales and Service for Business Applications such as Dynamics 365 and Salesforce. The book also introduces you to Copilot Studio to help you extend the copilots or build your own. By the end of this book, you will have a solid understanding of Microsoft Copilot and how to use copilots effectively and efficiently with various transformation platforms. You will also have a collection of tips, tricks, and best practices to help you get the most out of Microsoft Copilot. Microsoft Copilot uses a type of Machine Learning model known as a large language model (LLM). | |
Разместил: Ingvar16 30-07-2024, 01:54 | Комментарии: 0 | Подробнее
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