Exam Ref AZ-304 Microsoft Azure Architect Design Certification and Beyond. Design secure and reliable solutions for the real world in Microsoft Azure

Brett Hargreaves

The AZ-304 exam tests an architect's ability to design scalable, reliable, and secure solutions in Azure based on customer requirements. Exam Ref AZ-304 Microsoft Azure Architect Design Certification and Beyond offers complete, up-to-date coverage of the AZ-304 exam content to help you prepare for it confidently, pass the exam first time, and get ready for real-world challenges.This book will help you to investigate the need for good architectural practices and discover how they address common concerns for cloud-based solutions. You will work through the CloudStack, from identity and access through to infrastructure (IaaS), data, applications, and serverless (PaaS). As you make progress, you will delve into operations including monitoring, resilience, scalability, and disaster recovery. Finally, you'll gain a clear understanding of how these operations fit into the real world with the help of full scenario-based examples throughout the book.By the end of this Azure book, you'll have covered everything you need to pass the AZ-304 certification exam and have a handy desktop reference guide.

Autodesk Inventor 2023 Cookbook. A guide to gaining advanced modeling and automation skills for design engineers through actionable recipes

Alexander Bordino

Autodesk Inventor is an industry-leading, computer-aided design application for 3D mechanical design, simulation, visualization, and documentation. This book will help to bridge the gap between the fundamentals of this software and the more advanced features, workflows, and environments it has to offer.Using cookbook-style recipes, you’ll gain a comprehensive understanding and practical experience in creating dynamic 3D parts, assemblies, and complete designs. You’ll also explore a variety of topics, including automation and parametric techniques, collaboration tools, creating sheet metal designs, and design accelerators such as frame generators. As you progress, the chapters will guide you through surface modeling tools, advanced assembly, and simplification tools, along with covering iLogic, Finite Element Analysis, and more.By the end of this book, you’ll not only be able to use the advanced functionality within Autodesk Inventor but also have the practical experience you need to deploy specific techniques in your own projects and workflows.

Learning Bitcoin. Embrace the new world of fiance by leveraging the power of crypto-currencies using Bitcoin and the Blockchain

Richard Caetano, Matthew Stannard, Derek Wong

Extending Docker. Master the art of making Docker more extensible, composable, and modular by leveraging plugins and other supporting tools

Russ McKendrick

With Docker, it is possible to get a lot of apps running on the same old servers, making it very easy to package and ship programs. The ability to extend Docker using plugins and load third-party plugins is incredible, and organizations can massively benefit from it.In this book, you will read about what first and third party tools are available to extend the functionality of your existing Docker installation and how to approach your next Docker infrastructure deployment. We will show you how to work with Docker plugins, install it, and cover its lifecycle. We also cover network and volume plugins, and you will find out how to build your own plugin. You’ll discover how to integrate it with Puppet, Ansible, Jenkins, Flocker, Rancher, Packer, and more with third-party plugins. Then, you’ll see how to use Schedulers such as Kubernetes and Amazon ECS. Finally, we’ll delve into security, troubleshooting, and best practices when extending Docker.By the end of this book, you will learn how to extend Docker and customize it based on your business requirements with the help of various tools and plugins.

Learning Tableau 2022. Create effective data visualizations, build interactive visual analytics, and improve your data storytelling capabilities - Fifth Edition

Joshua N. Milligan, Blair Hutchinson, Mark Tossell, Roberto Andreoli

Learning Tableau 2022 helps you get started with Tableau and data visualization, but it does more than just cover the basic principles. It helps you understand how to analyze and communicate data visually, and articulate data stories using advanced features.This new edition is updated with Tableau’s latest features, such as dashboard extensions, Explain Data, and integration with CRM Analytics (Einstein Analytics), which will help you harness the full potential of artificial intelligence (AI) and predictive modeling in Tableau.After an exploration of the core principles, this book will teach you how to use table and level of detail calculations to extend and alter default visualizations, build interactive dashboards, and master the art of telling stories with data.You’ll learn about visual statistical analytics and create different types of static and animated visualizations and dashboards for rich user experiences. We then move on to interlinking different data sources with Tableau’s Data Model capabilities, along with maps and geospatial visualization. You will further use Tableau Prep Builder’s ability to efficiently clean and structure data.By the end of this book, you will be proficient in implementing the powerful features of Tableau 2022 to improve the business intelligence insights you can extract from your data.

Learning OpenCV 3 Application Development

Samyak Datta

Computer vision and machine learning concepts are frequently used in practical computer vision based projects. If you’re a novice, this book provides the steps to build and deploy an end-to-end application in the domain of computer vision using OpenCV/C++. At the outset, we explain how to install OpenCV and demonstrate how to run some simple programs. You will start with images (the building blocks of image processing applications), and see how they are stored and processed by OpenCV. You’ll get comfortable with OpenCV-specific jargon (Mat Point, Scalar, and more), and get to know how to traverse images and perform basic pixel-wise operations. Building upon this, we introduce slightly more advanced image processing concepts such as filtering, thresholding, and edge detection. In the latter parts, the book touches upon more complex and ubiquitous concepts such as face detection (using Haar cascade classifiers), interest point detection algorithms, and feature descriptors. You will now begin to appreciate the true power of the library in how it reduces mathematically non-trivial algorithms to a single line of code!The concluding sections touch upon OpenCV’s Machine Learning module. You will witness not only how OpenCV helps you pre-process and extract features from images that are relevant to the problems you are trying to solve, but also how to use Machine Learning algorithms that work on these features to make intelligent predictions from visual data!

OpenShift Multi-Cluster Management Handbook. Go from architecture to pipelines using GitOps

Giovanni Fontana, Rafael Pecora, Marcos Amorim

For IT professionals working with Red Hat OpenShift Container Platform, the key to maximizing efficiency is understanding the powerful and resilient options to maintain the software development platform with minimal effort. OpenShift Multi-Cluster Management Handbook is a deep dive into the technology, containing knowledge essential for anyone who wants to work with OpenShift.This book starts by covering the architectural concepts and definitions necessary for deploying OpenShift clusters. It then takes you through designing Red Hat OpenShift for hybrid and multi-cloud infrastructure, showing you different approaches for multiple environments (from on-premises to cloud providers). As you advance, you’ll learn container security strategies to protect pipelines, data, and infrastructure on each layer. You’ll also discover tips for critical decision making once you understand the importance of designing a comprehensive project considering all aspects of an architecture that will allow the solution to scale as your application requires.By the end of this OpenShift book, you’ll know how to design a comprehensive Red Hat OpenShift cluster architecture, deploy it, and effectively manage your enterprise-grade clusters and other critical components using tools in OpenShift Plus.

Hands-On Mathematics for Deep Learning. Build a solid mathematical foundation for training efficient deep neural networks

Jay Dawani

Most programmers and data scientists struggle with mathematics, having either overlooked or forgotten core mathematical concepts. This book uses Python libraries to help you understand the math required to build deep learning (DL) models.You'll begin by learning about core mathematical and modern computational techniques used to design and implement DL algorithms. This book will cover essential topics, such as linear algebra, eigenvalues and eigenvectors, the singular value decomposition concept, and gradient algorithms, to help you understand how to train deep neural networks. Later chapters focus on important neural networks, such as the linear neural network and multilayer perceptrons, with a primary focus on helping you learn how each model works. As you advance, you will delve into the math used for regularization, multi-layered DL, forward propagation, optimization, and backpropagation techniques to understand what it takes to build full-fledged DL models. Finally, you’ll explore CNN, recurrent neural network (RNN), and GAN models and their application.By the end of this book, you'll have built a strong foundation in neural networks and DL mathematical concepts, which will help you to confidently research and build custom models in DL.