Data Science with .NET and Polyglot Notebooks. Programmer's guide to data science using ML.NET, OpenAI, and Semantic Kernel

Matt Eland

As the fields of data science, machine learning, and artificial intelligence rapidly evolve, .NET developers are eager to leverage their expertise to dive into these exciting domains but are often unsure of how to do so. Data Science in .NET with Polyglot Notebooks is the practical guide you need to seamlessly bring your .NET skills into the world of analytics and AI. With Microsoft’s .NET platform now robustly supporting machine learning and AI tasks, the introduction of tools such as .NET Interactive kernels and Polyglot Notebooks has opened up a world of possibilities for .NET developers. This book empowers you to harness the full potential of these cutting-edge technologies, guiding you through hands-on experiments that illustrate key concepts and principles. Through a series of interactive notebooks, you’ll not only master technical processes but also discover how to integrate these new skills into your current role or pivot to exciting opportunities in the data science field. By the end of the book, you’ll have acquired the necessary knowledge and confidence to apply cutting-edge data science techniques and deliver impactful solutions within the .NET ecosystem.

LLVM Code Generation. A deep dive into compiler backend development

Quentin Colombet, Kristof Beyls

The LLVM infrastructure is a popular compiler ecosystem widely used in the tech industry and academia. This technology is crucial for both experienced and aspiring compiler developers looking to make an impact in the field. Written by Quentin Colombet, a veteran LLVM contributor and architect of the GlobalISel framework, this book provides a primer on the main aspects of LLVM, with an emphasis on its backend infrastructure; that is, everything needed to transform the intermediate representation (IR) produced by frontends like Clang into assembly code and object files.You’ll learn how to write an optimizing code generator for a toy backend in LLVM. The chapters will guide you step by step through building this backend while exploring key concepts, such as the ABI, cost model, and register allocation. You’ll also find out how to express these concepts using LLVM's existing infrastructure and how established backends address these challenges. Furthermore, the book features code snippets that demonstrate the actual APIs.By the end of this book, you’ll have gained a deeper understanding of LLVM. The concepts presented are expected to remain stable across different LLVM versions, making this book a reliable quick reference guide for understanding LLVM.

Mastering Windows 365. Deploy and Manage Cloud PCs and Windows 365 Link devices, Copilot with Intune, and Intune Suite - Second Edition

Christiaan Brinkhoff, Sandeep Patnaik, Morten Pedholt, Scott Manchester, ...

Windows 365 Cloud PC continues to evolve, integrating AI-driven management, enhanced security, and expanded capabilities to provide a seamless cloud-based Windows experience. This second edition builds on the foundation of the first, incorporating new content on Intune Suite, Copilot+ AI PCs, Windows App, and advanced security with Security Copilot to help IT professionals deploy, manage, and optimize Windows 365 Cloud PCs effectively.This edition expands beyond the basics, covering Intune Suite’s role in optimizing and securing deployments, new methods for application management and delivery, and insights into Windows 365 Link for hybrid cloud environments. You’ll also explore AI-powered administration with Security Copilot, providing intelligent security management and automation.Written by experts from the Windows 365 product team and a Microsoft MVP, this book provides practical guidance, best practices, and real-world insights to help you master modern Windows cloud management. Whether you’re working with Windows 365, Intune Suite, or AI-powered administration, this guide equips you with the latest tools and strategies to stay ahead in cloud computing.

Journey to Become a Google Cloud Machine Learning Engineer. Build the mind and hand of a Google Certified ML professional

Dr. Logan Song

This book aims to provide a study guide to learn and master machine learning in Google Cloud: to build a broad and strong knowledge base, train hands-on skills, and get certified as a Google Cloud Machine Learning Engineer.The book is for someone who has the basic Google Cloud Platform (GCP) knowledge and skills, and basic Python programming skills, and wants to learn machine learning in GCP to take their next step toward becoming a Google Cloud Certified Machine Learning professional.The book starts by laying the foundations of Google Cloud Platform and Python programming, followed the by building blocks of machine learning, then focusing on machine learning in Google Cloud, and finally ends the studying for the Google Cloud Machine Learning certification by integrating all the knowledge and skills together.The book is based on the graduate courses the author has been teaching at the University of Texas at Dallas. When going through the chapters, the reader is expected to study the concepts, complete the exercises, understand and practice the labs in the appendices, and study each exam question thoroughly. Then, at the end of the learning journey, you can expect to harvest the knowledge, skills, and a certificate.

Modern Network Observability. A hands-on approach using open source tools such as Telegraf, Prometheus, and Grafana

David Flores, Christian Adell, Josh VanDeraa, Eric Chou, ...

As modern IT services and software architectures such as microservices rely increasingly on network performance, the relevance of networks has never been greater. Network observability has emerged as a critical evolution of traditional monitoring, providing the deep visibility needed to manage today’s complex, dynamic environments. In Modern Network Observability, authors David Flores, Christian Adell, and Josh VanDeraa share their extensive experience to guide you through building and deploying a flexible observability stack using open-source tools.This book begins by addressing the limitations of monolithic monitoring solutions, showing you how to transform them into a composable, flexible observability stack. Through practical implementations, you’ll learn how to collect, normalize, and analyze network data from diverse sources, build intuitive dashboards, and set up actionable alerts that help you stay ahead of potential issues. Later, you’ll cover advanced topics, such as integrating observability data into your network automation strategy, ensuring your network operations align with business objectives.By the end of this book, you'll be able to proactively manage your network, minimize downtime, and ensure resilient, efficient, and future-proof operations.

Data Structures and Algorithms with the C++ STL. A guide for modern C++ practitioners

John Farrier

While the Standard Template Library (STL) offers a rich set of tools for data structures and algorithms, navigating its intricacies can be daunting for intermediate C++ developers without expert guidance. This book offers a thorough exploration of the STL’s components, covering fundamental data structures, advanced algorithms, and concurrency features.Starting with an in-depth analysis of the std::vector, this book highlights its pivotal role in the STL, progressing toward building your proficiency in utilizing vectors, managing memory, and leveraging iterators. The book then advances to STL’s data structures, including sequence containers, associative containers, and unordered containers, simplifying the concepts of container adaptors and views to enhance your knowledge of modern STL programming. Shifting the focus to STL algorithms, you’ll get to grips with sorting, searching, and transformations and develop the skills to implement and modify algorithms with best practices. Advanced sections cover extending the STL with custom types and algorithms, as well as concurrency features, exception safety, and parallel algorithms.By the end of this book, you’ll have transformed into a proficient STL practitioner ready to tackle real-world challenges and build efficient and scalable C++ applications.

Learn Microsoft Fabric. A practical guide to performing data analytics in the era of artificial intelligence

Arshad Ali, Bradley Schacht

Discover the capabilities of Microsoft Fabric, the premier unified solution designed for the AI era, seamlessly combining data integration, OneLake, transformation, visualization, universal security, and a unified business model. This book provides an overview of Microsoft Fabric, its components, and the wider analytics landscape.In this book, you'll explore workloads such as Data Factory, Synapse Data Engineering, data science, data warehouse, real-time analytics, and Power BI. You’ll learn how to build end-to-end lakehouse and data warehouse solutions using the medallion architecture, unlock the real-time analytics, and implement machine learning and AI models. As you progress, you’ll build expertise in monitoring workloads and administering Fabric across tenants, capacities, and workspaces. The book also guides you step by step through enhancing security and governance practices in Microsoft Fabric and implementing CI/CD workflows with Azure DevOps or GitHub. Finally, you’ll discover the power of Copilot, an AI-driven assistant that accelerates your analytics journey.By the end of this book, you’ll have unlocked the full potential of AI-driven data analytics, gaining a comprehensive understanding of the analytics landscape and mastery over the essential concepts and principles of Microsoft Fabric.

Artificial Intelligence for Robotics. Build intelligent robots using ROS 2, Python, OpenCV, and AI/ML techniques for real-world tasks - Second Edition

Francis X. Govers III, Dr. Kamesh Namuduri

Unlock the potential of your robots by enhancing their perception with cutting-edge artificial intelligence and machine learning techniques. From neural networks to computer vision, this second edition of the book equips you with the latest tools, new and expanded topics such as object recognition and creating artificial personality, and practical use cases to create truly smart robots.Starting with robotics basics, robot architecture, control systems, and decision-making theory, this book presents systems-engineering methods to design problem-solving robots with single-board computers. You'll explore object recognition using YOLO and genetic algorithms to teach your robot to identify and pick up objects, leverage natural language processing to give your robot a voice, and master neural networks to classify and separate objects and navigate autonomously, before advancing to guiding your robot arms using reinforcement learning and genetic algorithms. The book also covers path planning and goal-oriented programming to prioritize your robot's tasks, showing you how to connect all software using Python and ROS 2 for a seamless experience.By the end of this book, you'll have learned how to transform your robot into a helpful assistant with NLP and give it an artificial personality, ready to tackle real-world tasks and even crack jokes.