Applied Architecture Patterns on the Microsoft Platform. Work with various Microsoft technologies using Applied Architecture Patterns

Andre Dovgal, Dmitri Olechko, Gregor Noriskin

Applied Computational Thinking with Python. Algorithm design for complex real-world problems - Second Edition

Sofía De Jesús, Dayrene Martinez

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.

Applied Computational Thinking with Python. Design algorithmic solutions for complex and challenging real-world problems

Sofía De Jesús, Dayrene Martinez

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.

Applied Deep Learning and Computer Vision for Self-Driving Cars. Build autonomous vehicles using deep neural networks and behavior-cloning techniques

Sumit Ranjan, Dr. S. Senthamilarasu

Thanks to a number of recent breakthroughs, self-driving car technology is now an emerging subject in the field of artificial intelligence and has shifted data scientists' focus to building autonomous cars that will transform the automotive industry. This book is a comprehensive guide to use deep learning and computer vision techniques to develop autonomous cars. Starting with the basics of self-driving cars (SDCs), this book will take you through the deep neural network techniques required to get up and running with building your autonomous vehicle. Once you are comfortable with the basics, you'll delve into advanced computer vision techniques and learn how to use deep learning methods to perform a variety of computer vision tasks such as finding lane lines, improving image classification, and so on. You will explore the basic structure and working of a semantic segmentation model and get to grips with detecting cars using semantic segmentation. The book also covers advanced applications such as behavior-cloning and vehicle detection using OpenCV, transfer learning, and deep learning methodologies to train SDCs to mimic human driving.By the end of this book, you'll have learned how to implement a variety of neural networks to develop your own autonomous vehicle using modern Python libraries.

Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

Alex Galea, Luis Capelo

Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

Applied Machine Learning and High-Performance Computing on AWS. Accelerate the development of machine learning applications following architectural best practices

Mani Khanuja, Farooq Sabir, Shreyas Subramanian, Trenton Potgieter

Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you’ll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases.By the end of this book, you’ll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.

Applied SOA Patterns on the Oracle Platform. Fuse together your pragmatic Oracle experience with abstract SOA patterns with this practical guide

Sergey Popov

Applied Supervised Learning with Python. Use scikit-learn to build predictive models from real-world datasets and prepare yourself for the future of machine learning

Benjamin Johnston, Ishita Mathur

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!

Applied Unsupervised Learning with Python. Discover hidden patterns and relationships in unstructured data with Python

Benjamin Johnston, Aaron Jones, Christopher Kruger

Unsupervised learning is a useful and practical solution in situations where labeled data is not available.Applied Unsupervised Learning with Python guides you in learning the best practices for using unsupervised learning techniques in tandem with Python libraries and extracting meaningful information from unstructured data. The book begins by explaining how basic clustering works to find similar data points in a set. Once you are well-versed with the k-means algorithm and how it operates, you’ll learn what dimensionality reduction is and where to apply it. As you progress, you’ll learn various neural network techniques and how they can improve your model. While studying the applications of unsupervised learning, you will also understand how to mine topics that are trending on Twitter and Facebook and build a news recommendation engine for users. Finally, you will be able to put your knowledge to work through interesting activities such as performing a Market Basket Analysis and identifying relationships between different products.By the end of this book, you will have the skills you need to confidently build your own models using Python.

Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

Alok Malik, Bradford Tuckfield

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.

Applying Math with Python. Practical recipes for solving computational math problems using Python programming and its libraries

Sam Morley

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain.The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.

Apps and Services with .NET 7. Build practical projects with Blazor, .NET MAUI, gRPC, GraphQL, and other enterprise technologies

Mark J. Price

Apps and Services with .NET 7 is for .NET 6 and .NET 7 developers who want to kick their C# and .NET understanding up a gear by learning the practical skills and knowledge they need to build real-world applications and services. It covers specialized libraries that will help you monitor and improve performance, secure your data and applications, and internationalize your code and apps.With chapters that put a variety of technologies into practice, including Web API, OData, gRPC, GraphQL, SignalR, and Azure Functions, this book will give you a broader scope of knowledge than other books that often focus on only a handful of .NET technologies. It covers the latest developments, libraries, and technologies that will help keep you up to date.You’ll also leverage .NET MAUI to develop mobile apps for iOS and Android as well as desktop apps for Windows and macOS.

Apps and Services with .NET 8. Build practical projects with Blazor, .NET MAUI, gRPC, GraphQL, and other enterprise technologies - Second Edition

Mark J. Price

Elevate your practical C# and .NET skills to the next level with this new edition of Apps and Services with .NET 8.With chapters that put a variety of technologies into practice, including Web API, gRPC, GraphQL, and SignalR, this book will give you a broader scope of knowledge than other books that often focus on only a handful of .NET technologies. You’ll dive into the new unified model for Blazor Full Stack and leverage .NET MAUI to develop mobile and desktop apps.This new edition introduces the latest enhancements, including the seamless implementation of web services with ADO.NET SqlClient's native Ahead-of-Time (AOT) support. Popular library coverage now includes Humanizer and Noda Time. There’s also a brand-new chapter that delves into service architecture, caching, queuing, and robust background services.By the end of this book, you’ll have a wide range of best practices and deep insights under your belt to help you build rich apps and efficient services.

ArcGIS By Example. Develop three engaging ArcGIS applications to address your real-world mapping scenarios

Hussein Nasser

ArcGIS for Desktop Cookbook. Over 60 hands-on recipes to help you become a more productive ArcGIS for Desktop user

Daniela C Docan

ArcGIS Pro 2.x Cookbook. Create, manage, and share geographic maps, data, and analytical models using ArcGIS Pro

Tripp Corbin

ArcGIS is Esri's catalog of GIS applications with powerful tools for visualizing, maintaining, and analyzing data. ArcGIS makes use of the modern ribbon interface and 64-bit processing to increase the speed and efficiency of using GIS. It allows users to create amazing maps in both 2D and 3D quickly and easily. If you want to gain a thorough understanding of the various data formats that can be used in ArcGIS Pro and shared via ArcGIS Online, then this book is for you. Beginning with a refresher on ArcGIS Pro and how to work with projects, this book will quickly take you through recipes about using various data formats supported by the tool. You will learn the limits of each format, such as Shapefiles, Geodatabase, and CAD files, and learn how to link tables from outside sources to existing GIS data to expand the amount of data that can be used in ArcGIS. You'll learn methods for editing 2D and 3D data using ArcGIS Pro and how topology can be used to ensure data integrity. Lastly the book will show you how data and maps can be shared via ArcGIS Online and used with web and mobile applications.

ArcGIS Pro 3.x Cookbook. Create, manage, analyze, maintain, and visualize geospatial data using ArcGIS Pro - Second Edition

Tripp Corbin, GISP, Kirk Larson

ArcGIS Pro, Esri's newest desktop GIS application, offers powerful tools for visualizing, maintaining, and analyzing data. This cookbook will help existing ArcMap users transition to ArcGIS Pro and teach new users how to utilize its GIS tools effectively. You’ll learn how to create geodatabases, convert data formats, link tables from outside sources, edit 2D and 3D data, ensure data integrity with topology, and enable advanced geodatabase behavior. By the end, you'll be able to effectively use ArcGIS Pro as your primary desktop GIS application, maintaining, analyzing, and displaying data using common methods and tools.

Architecting ASP.NET Core Applications. An atypical design patterns guide for .NET 8, C# 12, and beyond - Third Edition

Carl-Hugo Marcotte, Nick Cosentino

This unique ASP.NET Core book will fill in the gaps in your REST API and backend designs. Learn how to build robust, maintainable, and flexible apps using Gang of Four (GoF) design patterns and modern architectural principles. This new edition is updated for .NET 8 and focuses exclusively on the backend, with new content on REST APIs, the REPR pattern, and building modular monoliths.You’ll start by covering foundational concepts like REST, the SOLID principles, Minimal APIs, dependency injection in .NET, and other ASP.NET Core 8 mechanisms. Then, you’ll learn to develop components using design patterns, including many from the GoF. Finally, you’ll explore organizing your application code with patterns that vary from layers to feature-oriented vertical slice designs, covering CQS and a deep dive into microservices along the way. A brand-new e-commerce project at the end of the book will tie it all together.This how-to guide will teach you how to assemble your own APIs from building blocks, to suit whatever real-world requirements you may have.

Architecting AWS with Terraform. Design resilient and secure Cloud Infrastructures with Terraform on Amazon Web Services

Erol Kavas

Infrastructure as code (IaC) and Terraform have become essential tools for managing cloud infrastructure and automating deployment processes. Mastering Terraform on AWS presents a step-by-step approach on how to master these tools and build a secure and scalable AWS infrastructure. The book starts with an introduction to patterns and anti-patterns of IaC and Terraform, explaining the common mistakes and pitfalls you must avoid. You’ll then learn about the importance of planning and designing infrastructure projects in AWS and making informed decisions for projects in AWS Terraform. You’ll find out how to implement Terraform in your projects and deploy serverless projects. Next, you’ll be able to deploy containers in AWS with Terraform, exploring the best practices for Terraform IaC projects. You’ll understand how you can leverage Terraform for enterprises, build Git workflows for IaC and Terraform projects, and automate deployment of Terraform projects. The last set of chapters will teach you how to govern AWS with Terraform and build a secure infrastructure. Finally, you’ll discover how to achieve a perfect AWS infrastructure with Terraform. By the end of this book, you’ll have gained a comprehensive understanding of the various aspects of Terraform and IaC, along with the knowledge required to build, manage, and deploy complex infrastructures on AWS.

Architecting Cloud Computing Solutions. Build cloud strategies that align technology and economics while effectively managing risk

Kevin L. Jackson, Scott Goessling

Cloud adoption is a core component of digitaltransformation. Scaling the IT environment,making it resilient, and reducing costs arewhat organizations want. Architecting CloudComputing Solutions presents and explainscritical cloud solution design considerationsand technology decisions required tobe made for deploying the right cloudservice and deployment models,based on your business andtechnology service requirements.This book starts with the fundamentalsof cloud computing and its architecturalconcepts. It then walks you throughcloud service models (IaaS, PaaS, andSaaS), deployment models (public,private, community, and hybrid)and implementation options(enterprise, MSP, and CSP) to explainand describe the key considerations andchallenges organizations face during cloudmigration. Later, this book delves into how toleverage DevOps, Cloud-Native, and serverlessarchitectures in your cloud environmentand presents industry best practices forscaling your cloud environment. Finally,this book addresses in depth how tomanage essential cloud technologyservice components, such as data storage,security controls, and disaster recovery.By the end of this book, you will havemastered all the design considerations andoperational trades required to adopt cloudservices, no matter which cloud serviceprovider you choose.

Architecting Cloud-Native Serverless Solutions. Design, build, and operate serverless solutions on cloud and open source platforms

Safeer CM

Serverless computing has emerged as a mainstream paradigm in both cloud and on-premises computing, with AWS Lambda playing a pivotal role in shaping the Function-as-a-Service (FaaS) landscape. However, with the explosion of serverless technologies and vendors, it has become increasingly challenging to comprehend the foundational services and their offerings.Architecting Cloud Native Serverless Solutions lays a strong foundation for understanding the serverless landscape and technologies in a vendor-agnostic manner. You'll learn how to select the appropriate cloud vendors and technologies based on your specific needs. In addition, you'll dive deep into the serverless services across AWS, GCP, Azure, and Cloudflare followed by open source serverless tools such as Knative, OpenFaaS, and OpenWhisk, along with examples. You'll explore serverless solutions on Kubernetes that can be deployed on both cloud-hosted clusters and on-premises environments, with real-world use cases. Furthermore, you'll explore development frameworks, DevOps approaches, best practices, security considerations, and design principles associated with serverless computing.By the end of this serverless book, you'll be well equipped to solve your business problems by using the appropriate serverless vendors and technologies to build efficient and cost-effective serverless systems independently.

Architecting Google Cloud Solutions. Learn to design robust and future-proof solutions with Google Cloud technologies

Victor Dantas

Google has been one of the top players in the public cloud domain thanks to its agility and performance capabilities. This book will help you design, develop, and manage robust, secure, and dynamic solutions to successfully meet your business needs.You'll learn how to plan and design network, compute, storage, and big data systems that incorporate security and compliance from the ground up. The chapters will cover simple to complex use cases for devising solutions to business problems, before focusing on how to leverage Google Cloud's Platform-as-a-Service (PaaS) and Software-as-a-Service (SaaS) capabilities for designing modern no-operations platforms. Throughout this book, you'll discover how to design for scalability, resiliency, and high availability. Later, you'll find out how to use Google Cloud to design modern applications using microservices architecture, automation, and Infrastructure-as-Code (IaC) practices. The concluding chapters then demonstrate how to apply machine learning and artificial intelligence (AI) to derive insights from your data. Finally, you will discover best practices for operating and monitoring your cloud solutions, as well as performing troubleshooting and quality assurance.By the end of this Google Cloud book, you'll be able to design robust enterprise-grade solutions using Google Cloud Platform.

Architecting Modern Java EE Applications. Designing lightweight, business-oriented enterprise applications in the age of cloud, containers, and Java EE 8

Sebastian Daschner

Java EE 8 brings with it a load of features, mainly targeting newer architectures such as microservices, modernized security APIs, and cloud deployments. This book will teach you to design and develop modern, business-oriented applications using Java EE 8. It shows how to structure systems and applications, and how design patterns and Domain Driven Design aspects are realized in the age of Java EE 8. You will learn about the concepts and principles behind Java EE applications, and how to effect communication, persistence, technical and cross-cutting concerns, and asynchronous behavior.This book covers Continuous Delivery, DevOps, infrastructure-as-code, containers, container orchestration technologies, such as Docker and Kubernetes, and why and especially how Java EE fits into this world. It also covers the requirements behind containerized, zero-dependency applications and how modern Java EE application servers support these approaches. You will also learn about automated, fast, and reliable software tests, in different test levels, scopes, and test technologies. This book covers the prerequisites and challenges of distributed systems that lead to microservice, shared-nothing architectures. The challenges and solutions of consistency versus scalability will further lead us to event sourcing, event-driven architectures, and the CQRS principle. This book also includes the nuts and bolts of application performance as well as how to realize resilience, logging, monitoring and tracing in a modern enterprise world. Last but not least the demands of securing enterprise systems are covered.By the end, you will understand the ins and outs of Java EE so that you can make critical design decisions that not only live up to, but also surpass your clients' expectations.

Architecting the Industrial Internet. The architect's guide to designing Industrial Internet solutions

Robert Stackowiak, Shyam Varan Nath, Carla Romano

The Industrial Internet or the IIoT has gained a lot of traction. Many leading companies are driving this revolution by connecting smart edge devices to cloud-based analysis platforms and solving their business challenges in new ways. To ensure a smooth integration of such machines and devices, sound architecture strategies based on accepted principles, best practices, and lessons learned must be applied.This book begins by providing a bird's eye view of what the IIoT is and how the industrial revolution has evolved into embracing this technology. It then describes architectural approaches for success, gathering business requirements, and mapping requirements into functional solutions. In a later chapter, many other potential use cases are introduced including those in manufacturing and specific examples in predictive maintenance, asset tracking and handling, and environmental impact and abatement. The book concludes by exploring evolving technologies that will impact IIoT architecture in the future and discusses possible societal implications of the Industrial Internet and perceptions regarding these projects.By the end of this book, you will be better equipped to embrace the benefits of the burgeoning IIoT.