Название: AutonoML: Towards an Integrated Framework for Autonomous Machine Learning
Автор: David Jacob Kedziora, Katarzyna Musial, Bogdan Gabrys
Издательство: Now Foundations and Trends
Год выхода: 2024
Страниц: 186
Формат: PDF
Размер: 10,4 MB
Язык: английский

Over the last decade, the long-running endeavour to automate high-level processes in machine learning (ML) has risen to mainstream prominence. Beyond this, an even loftier goal is the pursuit of autonomy, which describes the capability of the system to independently adjust an ML solution over a lifetime of changing contexts. This monograph provides an expansive perspective on what constitutes an automated/autonomous ML system. In doing so, the authors survey developments in hyperparameter optimisation, multicomponent models, neural architecture search, automated feature engineering, meta-learning, multi-level ensembling, dynamic adaptation, multi-objective evaluation, resource constraints, flexible user involvement, and the principles of generalisation. Furthermore, they develop a conceptual framework throughout to illustrate one possible way of fusing high-level mechanisms into an autonomous ML system. This monograph lays the groundwork for students and researchers to understand the factors limiting architectural integration, without which the field of automated ML risks stifling both its technical advantages and general uptake.

Название: Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: Artificial Intelligence Applications
Автор: Irfan Ali, Umar Muhammad Modibbo, Asaju La’aro Bolaji, Harish Garg
Издательство: CRC Press
Год: 2025
Страниц: 228
Язык: английский
Формат: pdf (true), epub
Размер: 13.0 MB

This book comprehensively discusses nature-inspired algorithms, Deep Learning methods, applications of mathematical programming and Artificial Intelligence techniques. It will further cover important topic such as linking green supply chain management practices with competitiveness, industry 4.0, and social responsibility. In today’s hyper‑connected digital landscape, the demand for seamless and efficient computing resources has skyrocketed, driven by the proliferation of Internet of Things (IoT) devices, edge computing, and data‑intensive applications. The convergence of fog and cloud computing has emerged as a promising solution to meet these escalating computational requirements. Fog computing, characterized by its proximity to edge devices, brings computation and data storage closer to the point of data generation, reducing latency and improving real‑time processing. Machine Learning techniques, particularly Deep Learning and Reinforcement Learning, have demonstrated remarkable capabilities in handling complex and dynamic scenarios. When combined with multi‑objective optimization approaches, they can significantly enhance load balancing in integrated fog‑cloud environments. This integration enables decision‑making processes that consider various objectives simultaneously, such as minimizing latency, maximizing energy efficiency, and ensuring resource availability.

Название: Optimization and Computing using Intelligent Data-Driven Approaches for Decision-Making: Optimization Applications
Автор: Irfan Ali, Umar Muhammad Modibbo, Asaju La’aro Bolaji, Harish Garg
Издательство: CRC Press
Год: 2025
Страниц: 335
Язык: английский
Формат: pdf (true), epub
Размер: 19.0 MB

This book comprehensively discusses nature‑inspired algorithms, deep learning methods, applications of mathematical programming, and Artificial Intelligence techniques. It further covers important topics such as the use of Machine Learning and the Internet of Things and multi‑objective optimization under Fermatean hesitant fuzzy and uncertain environment. Data Science has risen as a comprehensive field that utilizes statistical methods, data analysis, and related techniques to comprehend and scrutinize phenomena through data. Sophisticated analytics techniques, inclusive of Machine Learning models, are utilized to derive actionable intelligence or profound understanding from data. This procedure of converting raw data into significant insights is recognized as data‑driven decision‑making (DDDM). The text is primarily written for graduate students and academic researchers in diverse fields, including operations research, mathematics, statistics, Computer Science, information and communication technology, and industrial engineering.

Название: C++ & Python for Beginners - 20th Edition 2024
Автор: Papercut Limited
Издательство: Papercut Limited
Год: 2024
Язык: английский
Формат: pdf
Размер: 40.3 MB

Изучите основы Python и C++ и расширьте свои навыки программирования! Высококачественный справочник, содержащий подробные руководства от команды экспертов. Изучайте Python и применяйте его в реальных программах. Начните изучать основы C++ и лучшие советы по работе с кодом. Python и C++ - два самых мощных и многофункциональных языка программирования. Умение понимать и использовать любой из них позволит вам лучше понять современные технологии и то, как они взаимодействуют с нами и окружением.

Название: Learn Data Science Using Python: A Quick-Start Guide
Автор: Engy Fouda
Издательство: Apress
Год: 2024
Страниц: 190
Язык: английский
Формат: pdf (true), epub
Размер: 14.0 MB

Harness the capabilities of Python and gain the expertise need to master Data Science techniques. This step-by-step book guides you through using Python to achieve tasks related to data cleaning, statistics, and visualization. You'll start by reviewing the foundational aspects of the Data Science process. This includes an extensive overview of research points and practical applications, such as the insightful analysis of presidential elections. The journey continues by navigating through installation procedures and providing valuable insights into Python, data types, typecasting, and essential libraries like Pandas and NumPy. You'll then delve into the captivating world of data visualization. Concepts such as scatter plots, histograms, and bubble charts come alive through detailed discussions and practical code examples, unraveling the complexities of creating compelling visualizations for enhanced data understanding. For Data Analysts, data scientists, Python programmers, and software developers new to Data Science.

Название: Optimizing Generative AI Workloads for Sustainability: Balancing Performance and Environmental Impact in Generative AI
Автор: Ishneet Kaur Dua, Parth Girish Patel
Издательство: Apress
Год: 2024
Страниц: 328
Язык: английский
Формат: pdf
Размер: 13.3 MB

This comprehensive guide provides practical strategies for optimizing Generative AI systems to be more sustainable and responsible. As advances in Generative AI such as LLMs accelerate, optimizing these resource-intensive workloads for efficiency and alignment with human values grows increasingly urgent. The book starts with the concept of Generative AI and its wide-ranging applications, while also delving into the environmental impact of AI workloads and the growing importance of adopting sustainable AI practices. It then delves into the fundamentals of efficient AI workload management, providing insights into understanding AI workload characteristics, measuring performance, and identifying bottlenecks and inefficiencies. Hardware optimization strategies are explored in detail, covering the selection of energy-efficient hardware, leveraging specialized AI accelerators, and optimizing hardware utilization and scheduling for sustainable operations. You are also guided through software optimization techniques tailored for Generative AI, including efficient model architecture, compression, and quantization methods, and optimization of software libraries and frameworks.

Название: Mastering Serverless Computing with AWS Lambda: Unlock Scalability, Optimize Costs, and Drive Innovation with AWS Lambda Serverless Solutions for Modern Cloud Transformation
Автор: Eidivandi Omid
Издательство: Orange Education Pvt Ltd
Год: 2024
Страниц: 317
Язык: английский
Формат: pdf, epub
Размер: 35.1 MB

Design and Build Scalable Solutions on the AWS Serverless Ecosystem. AWS Lambda, a key component of AWS Serverless Computing, has transformed application development by allowing developers to focus on code rather than infrastructure. [Mastering Serverless Computing with AWS Lambda] is a must-have guide for leveraging AWS Lambda to build efficient, cost-effective serverless cloud solutions. This book guides readers from serverless basics to advanced deployment, offering practical approaches to building resilient, scalable applications. Beginning with an introduction to serverless computing, the book explores AWS Lambda fundamentals, covering invocation models, service integrations, and event-driven design. Practical insights into hyper-scaling, instrumentation, and designing for failure empower readers to create robust, production-ready solutions. This guide covers core concepts of serverless computing, including optimizations, automation, and strategies to navigate potential pitfalls. It emphasizes AWS Lambda’s resiliency, scalability, and disaster recovery, using real-world examples to showcase best practices.

Название: Data Science Solutions on Azure: The Rise of Generative AI and Applied AI, 2nd Edition
Автор: Julian Soh, Priyanshi Singh
Издательство: Apress
Год: 2024
Страниц: 294
Язык: английский
Формат: pdf
Размер: 18.9 MB

This revamped and updated book focuses on the latest in AI technology―Generative AI. It builds on the first edition by moving away from traditional data science into the area of applied AI using the latest breakthroughs in Generative AI. Based on real-world projects, this edition takes a deep look into new concepts and approaches such as Prompt Engineering, testing and grounding of Large Language Models, fine tuning, and implementing new solution architectures such as Retrieval Augmented Generation (RAG). You will learn about new embedded AI technologies in Search, such as Semantic and Vector Search. Written with a view on how to implement Generative AI in software, this book contains examples and sample code. In addition to traditional Data Science experimentation in Azure Machine Learning (AML) that was covered in the first edition, the authors cover new tools such as Azure AI Studio, specifically for testing and experimentation with Generative AI models.

Название: Building Secure PHP Applications: A Comprehensive Guide to Protecting Your Web Applications from Threats
Автор: Satej Kumar Sahu
Издательство: Apress
Год: 2024
Страниц: 437
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB

Learn how to protect PHP applications from potential vulnerabilities and attacks. As cyberattacks and data breaches continue to rise, it's crucial for developers and organizations to prioritize security in their PHP applications. The book offers an all-encompassing guide to securing PHP applications, covering topics ranging from PHP core security to web security, framework security (with a focus on Laravel), security standards, and protocol security. After examining PHP core security and essential topics, such as input validation, output encoding, secure session management, and secure file handling, you’ll move on to common security risks in PHP applications and provides practical examples to demonstrate effective security measures. From there, you’ll delve into web security, addressing XSS, SQL injection, and CSRF, reviewing in-depth explanations and mitigation techniques. Primarily written for developers, security professionals, and webmasters involved in PHP application development. Additionally, this book may be used as a reference for students studying web development, PHP programming or cybersecurity.

Название: Artificial Intelligence for Precision Agriculture
Автор: Pethuru Raj, N. Gayathri, G. Jaspher Willsie Kathrine
Издательство: CRC Press
Год: 2025
Страниц: 322
Язык: английский
Формат: pdf (true), epub
Размер: 25.5 MB

Precision agriculture is a next-generation farming management concept that optimizes resource use, productivity, quality, profitability, and sustainability by observing and responding to crop variability. Precision agriculture employs digital technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), 5G communication, cybersecurity, edge computing, cloud-native principles, and blockchain to ensure crops and soil receive exactly what they need for optimal health and productivity. Artificial Intelligence for Precision Agriculture explores the latest developments in precision agriculture, detailing how AI contributes to its goals. The book discusses how precision agriculture solutions use IoT devices, data storage, AI analytics, connectivity, and cloud infrastructures to analyze factors such as soil type, terrain, weather, plant growth, and yield data. It also examines edge technologies—sensors, microchips, beacons, RFID tags, robots, drones, and actuators—that collect field data and transmit it to cloud-based AI platforms for analysis. The book shows how AI-driven insights guide actions in the field, such as crop rotation, optimal planting and harvesting times, and soil management, and help farmers apply the right amounts of water, fertilizers, and pesticides, reducing waste and environmental impact. Machine Learning is a technique that uses mathematics and statistical learning to create a model that helps in predicting the values which are not known prior. In Machine Learning, we try to solve problems with computers without explicitly programming them for a specific task.