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 Название: Ultimate Agentic AI with AutoGen for Enterprise Automation: Design, Build, And Deploy Enterprise-Grade AI Agents Using LLMs and AutoGen To Power Intelligent, Scalable Enterprise Automation
Автор: Shekhar Agrawal, Srinivasa Sunil Chippada, Rathish Mohan
Издательство: Orange Education Pvt Ltd, AVA
Год: 2025
Страниц: 365
Язык: английский
Формат: epub (true)
Размер: 53.0 MB
Empowering Enterprises with Scalable, Intelligent AI Agents. In an era where Artificial Intelligence (AI) is transforming enterprises, Large Language Models (LLMs) are unlocking new frontiers in automation, augmentation, and intelligent decision-making. Ultimate Agentic AI with AutoGen for Enterprise Automation bridges the gap between foundational AI concepts and hands-on implementation, empowering professionals to build scalable and intelligent enterprise agents. The book begins with the core principles of LLM agents and gradually moves into advanced topics such as agent architecture, tool integration, memory systems, and context awareness. Readers will learn how to design task-specific agents, apply ethical and security guardrails, and operationalize them using the powerful AutoGen framework. This book is tailored for AI/ML engineers, software developers, data scientists, solution architects, enterprise tech leads, product managers, innovation strategists, and CTOs. | |
Разместил: Ingvar16 Вчера, 02:05 | Комментарии: 0 | Подробнее
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 Название: Probing the Past with Data Analytics and AI
Автор: Marc Thuillard
Издательство: World Scientific Publishing
Год: 2025
Страниц: 260
Язык: английский
Формат: pdf (true)
Размер: 11.4 MB
This comprehensive compendium explains the technical challenges and opportunities behind the most recent and successful applications in Artificial Intelligence [AI] and data analytics. It focuses on applications that have the power to be adapted to many different fields and explains how AI can be implemented as an assistant in digital humanities. It also introduces new methods and applications in classification trees, networks, and Bayesian learning. The useful reference text benefits professionals, academics, researchers, and graduate students in AI/Machine Learning, neural networks, and bioinformatics, and digital humanities. The first chapter briefly introduces the most important classical classification techniques. The choice was set on methods that find broad applications in data analytics and AI further in the book. In this chapter, as in the rest of this book, we follow the strategy to explain and illustrate the main ideas behind the most advanced AI with minimal use of equations. The reader is furnished with many good references to enquire further. The second chapter is on a family of Neural Networks described by the acronym “CNN”, which stands for convolutional neural networks. | |
Разместил: Ingvar16 Вчера, 01:25 | Комментарии: 0 | Подробнее
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 Название: Visual Object Tracking: An Evaluation Perspective
Автор: Xin Zhao, Shiyu Hu, Xu-Cheng Yin
Издательство: Springer
Серия: Advances in Computer Vision and Pattern Recognition
Год: 2025
Страниц: 202
Язык: английский
Формат: pdf (true)
Размер: 21.1 MB
This book delves into visual object tracking (VOT), a fundamental aspect of computer vision crucial for replicating human dynamic vision, with applications ranging from self-driving vehicles to surveillance systems. Despite significant strides propelled by Deep Learning, challenges such as target deformation and motion persist, exposing a disparity between cutting-edge VOT systems and human performance. This observation underscores the necessity to thoroughly scrutinize and enhance evaluation methodologies within VOT research. Hence, the primary objective of this book is to equip readers with essential insights into dynamic visual tasks encapsulated by VOT. Beginning with the elucidation of task definitions, it integrates interdisciplinary perspectives on evaluation techniques. Pursuing dynamic visual intelligence represents one of the most significant challenges in Artificial Intelligence (AI), requiring the synthesis of perceptual acuity, cognitive reasoning, and real-time adaptability. This book addresses these challenges, positioning itself at the intersection of cognitive science, Computer Vision, and Machine Learning. | |
Разместил: Ingvar16 2-07-2025, 22:13 | Комментарии: 0 | Подробнее
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 Название: Derivative-Free Optimization: Theoretical Foundations, Algorithms, and Applications
Автор: Yang Yu, Hong Qian, Yi-Qi Hu
Издательство: Springer
Серия: Machine Learning: Foundations, Methodologies, and Applications
Год: 2025
Страниц: 194
Язык: английский
Формат: pdf (true), epub
Размер: 34.1 MB
This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in Artificial Intelligence (AI), Machine Learning (ML), AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences. The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book’s structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML. A foundational understanding of Machine Learning, probability theory, and algorithms is recommended for readers to fully engage with the material. | |
Разместил: Ingvar16 2-07-2025, 21:37 | Комментарии: 0 | Подробнее
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 Название: Practical Software Project Management: Design and track execution models, and manage dependencies, changes, and project issues
Автор: Abhi Basu Thakur
Издательство: BPB Publications
Год: 2025
Страниц: 408
Язык: английский
Формат: pdf, epub (true)
Размер: 10.7 MB
Managing software projects in today's fast-paced technological landscape is crucial for success, demanding a clear understanding of processes, people, and products. Practical Software Project Management serves as your essential guide, transforming complex project lifecycles into a manageable and actionable roadmap. This book systematically covers the entire project journey, beginning with project initiation, objective definition, and crucial stakeholder identification. You will learn practical estimation techniques and planning strategies, alongside effective team management, including recruitment, conflict resolution, and motivation. The guide progresses through essential requirement analysis, robust architecture, and design phases, and explains how to execute successful project kickoffs. Automation helps in automatically testing the product. Some of the well-known products that are used to automate testing are Selenium, TestNG, Postman, and many more. There are many tools that can be used. There should be an appropriate selection criterion for selecting the tool. Many languages can be used for testing. Python and Java are popular languages used in test automation. This book is for software developers, project managers, program managers, and corporate trainers seeking to master practical software project management. | |
Разместил: Ingvar16 2-07-2025, 20:16 | Комментарии: 0 | Подробнее
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 Название: High-performance Algorithmic Trading using Machine Learning: Building automated trading startegies with AutoML and feature engineering
Автор: Franck Bardol
Издательство: BPB Publications
Год: 2025
Страниц: 398
Язык: английский
Формат: epub (true)
Размер: 49.3 MB
Machine Learning is not just an advantage; it is becoming standard practice among top-performing trading firms. As traditional strategies struggle to navigate noise, complexity, and speed, ML-powered systems extract alpha by identifying transient patterns beyond human reach. This shift is transforming how hedge funds, quant teams, and algorithmic platforms operate, and now, these same capabilities are available to advanced practitioners. This book is a practitioner’s blueprint for building production-grade ML trading systems from scratch. It goes far beyond basic return-sign classification tasks, which often fail in live markets, and delivers field-tested techniques used inside elite quant desks. It covers everything from the fundamentals of systematic trading and ML's role in detecting patterns to data preparation, backtesting, and model lifecycle management using Python libraries. You will learn to implement supervised learning for advanced feature engineering and sophisticated ML models. This book is for robo traders, algorithmic traders, hedge fund managers, portfolio managers, Python developers, engineers, and analysts who want to understand, master, and integrate Machine Learning into trading strategies. Readers should understand basic automated trading concepts and have some beginner experience writing Python code. | |
Разместил: Ingvar16 2-07-2025, 19:14 | Комментарии: 0 | Подробнее
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 Название: AI and Microservices: Integrating AI into API Design and Distributed Microservice Architecture
Автор: Dileep Kumar Pandiya, Nilesh Charankar
Издательство: Apress
Год: 2025
Страниц: 345
Язык: английский
Формат: pdf (true), epub
Размер: 13.4 MB
This book explores how Artificial Intelligence (AI) is transforming the design and operation of microservices and API architecture. It provides a clear and practical guide to using AI to automate tasks, enhance performance, and improve the scalability of microservice-based systems. Starting with the basics, you will learn about the core concepts of microservices and API design, gradually building an understanding of how AI can be seamlessly integrated. Through real-world examples, visual diagrams, and mock APIs, the book shows you how to bring theory into practice, making complex systems easier to manage and more efficient. You will also discover strategies for testing and scaling systems, securing APIs, and addressing ethical challenges in AI-powered environments. Case studies highlight successful implementations, offering valuable insights you can apply to your own projects. For Software architects and engineers, AI and Machine Learning professionals, and DevOps engineers. | |
Разместил: Ingvar16 2-07-2025, 18:03 | Комментарии: 0 | Подробнее
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 Название: Intermediate Python and Large Language Models
Автор: Dilyan Grigorov
Издательство: Apress
Год: 2025
Страниц: 342
Язык: английский
Формат: pdf (true), epub (true)
Размер: 10.1 MB
Harness the power of Large Language Models (LLMs) to build cutting-edge AI applications with Python and LangChain. This book provides a hands-on approach to understanding, implementing, and deploying LLM-powered solutions, equipping developers, data scientists, and AI enthusiasts with the tools to create real-world AI applications. The journey begins with an introduction to LangChain, covering its core concepts, integration with Python, and essential components such as prompt engineering, memory management, and retrieval-augmented generation (RAG). As you progress, you’ll explore advanced AI workflows, including multi-agent architectures, fine-tuning strategies, and optimization techniques to maximize LLM efficiency. The book also takes a deep dive into practical applications of LLMs, guiding you through the development of intelligent chatbots, document retrieval systems, content generation pipelines, and AI-driven automation tools. You’ll learn how to leverage APIs, integrate LLMs into web and mobile platforms, and optimize large-scale deployments while addressing key challenges such as inference latency, cost efficiency, and ethical considerations. | |
Разместил: Ingvar16 2-07-2025, 08:56 | Комментарии: 0 | Подробнее
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 Название: How to Code Python with AI: A Beginner's Guide to Learning Coding using AI
Автор: Laurence Lars Svekis
Издательство: Independently published
Год: 2025
Страниц: 238
Язык: английский
Формат: pdf, epub, mobi
Размер: 10.1 MB
Unlock the world of Python programming with the power of artificial intelligence at your side!“How to Code Python with AI” is a revolutionary beginner’s guide that redefines how coding is learned—by teaming up with today’s most advanced AI tools like ChatGPT. Whether you're brand new to programming or have tried and struggled before, this book provides a fresh, empowering, and incredibly accessible path to mastering Python through interactive learning, hands-on practice, and AI-assisted support. Written by best-selling author and coding educator Laurence Lars Svekis, this book walks you through each foundational concept of Python programming in a simple, friendly, and visual way. Each chapter includes engaging examples, clear explanations, interactive exercises, and smart AI prompts to accelerate your learning and build your confidence. By the end of this book, you'll not only be writing Python programs with confidence but also leveraging AI as your powerful coding co-pilot. Start your journey today, and experience a whole new way to code—guided, supported, and accelerated by the intelligence of AI. | |
Разместил: Ingvar16 2-07-2025, 07:33 | Комментарии: 0 | Подробнее
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 Название: Introduction to Foundation Models
Автор: Pin-Yu Chen, Sijia Liu
Издательство: Springer
Год: 2025
Страниц: 307
Язык: английский
Формат: pdf (true), epub
Размер: 42.8 MB
This book offers an extensive exploration of foundation models, guiding readers through the essential concepts and advanced topics that define this rapidly evolving research area. Designed for those seeking to deepen their understanding and contribute to the development of safer and more trustworthy AI technologies, the book is divided into three parts providing the fundamentals, advanced topics in foundation modes, and safety and trust in Foundation Models. Part I introduces the core principles of foundation models and Generative AI, presents the technical background of neural networks, delves into the learning and generalization of transformers, and finishes with the intricacies of transformers and in-context learning. Part II introduces automated visual prompting techniques, prompting LLMs with privacy, memory-efficient fine-tuning methods, and shows how LLMs can be reprogrammed for time-series Machine Learning tasks. It explores how LLMs can be reused for speech tasks, how synthetic datasets can be used to benchmark foundation models, and elucidates machine unlearning for foundation models. | |
Разместил: Ingvar16 2-07-2025, 06:55 | Комментарии: 0 | Подробнее
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