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Python Machine Learning By Example - Yuxi Liu - Bog - Packt Publishing Limited - Plusbog.dk

Python Machine Learning By Example - Yuxi Liu - Bog - Packt Publishing Limited - Plusbog.dk

Author Yuxi (Hayden) Liu teaches machine learning from the fundamentals to building NLP transformers and multimodal models with best practice tips and real-world examples using PyTorch, TensorFlow, scikit-learn, and pandasKey FeaturesDiscover new and updated content on NLP transformers, PyTorch, and computer vision modelingIncludes a dedicated chapter on best practices and additional best practice tips throughout the book to improve your ML solutionsImplement ML models, such as neural networks and linear and logistic regression, from scratchPurchase of the print or Kindle book includes a free PDF copyBook DescriptionThe fourth edition of Python Machine Learning By Example is a comprehensive guide for beginners and experienced machine learning practitioners who want to learn more advanced techniques, such as multimodal modeling. Written by experienced machine learning author and ex-Google machine learning engineer Yuxi (Hayden) Liu, this edition emphasizes best practices, providing invaluable insights for machine learning engineers, data scientists, and analysts. Explore advanced techniques, including two new chapters on natural language processing transformers with BERT and GPT, and multimodal computer vision models with PyTorch and Hugging Face. You’ll learn key modeling techniques using practical examples, such as predicting stock prices and creating an image search engine. This hands-on machine learning book navigates through complex challenges, bridging the gap between theoretical understanding and practical application. Elevate your machine learning and deep learning expertise, tackle intricate problems, and unlock the potential of advanced techniques in machine learning with this authoritative guide.What you will learnFollow machine learning best practices throughout data preparation and model developmentBuild and improve image classifiers using convolutional neural networks (CNNs) and transfer learningDevelop and fine-tune neural networks using TensorFlow and PyTorchAnalyze sequence data and make predictions using recurrent neural networks (RNNs), transformers, and CLIPBuild classifiers using support vector machines (SVMs) and boost performance with PCAAvoid overfitting using regularization, feature selection, and moreWho this book is forThis expanded fourth edition is ideal for data scientists, ML engineers, analysts, and students with Python programming knowledge. The real-world examples, best practices, and code prepare anyone undertaking their first serious ML project.

DKK 428.00
1

Machine Learning with PyTorch and Scikit-Learn - Sebastian Raschka - Bog - Packt Publishing Limited - Plusbog.dk

Machine Learning with PyTorch and Scikit-Learn - Sebastian Raschka - Bog - Packt Publishing Limited - Plusbog.dk

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key FeaturesLearn applied machine learning with a solid foundation in theoryClear, intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practicesBook DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch?PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learnExplore frameworks, models, and techniques for machines to learn from dataUse scikit-learn for machine learning and PyTorch for deep learningTrain machine learning classifiers on images, text, and moreBuild and train neural networks, transformers, and boosting algorithmsDiscover best practices for evaluating and tuning modelsPredict continuous target outcomes using regression analysisDig deeper into textual and social media data using sentiment analysisWho this book is forIf you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

DKK 503.00
1

Hands-On Genetic Algorithms with Python - Eyal Wirsansky - Bog - Packt Publishing Limited - Plusbog.dk

Hands-On Genetic Algorithms with Python - Eyal Wirsansky - Bog - Packt Publishing Limited - Plusbog.dk

Explore the ever-growing world of genetic algorithms to solve search, optimization, and AI-related tasks, and improve machine learning models using Python libraries such as DEAP, scikit-learn, and NumPyKey FeaturesExplore the ins and outs of genetic algorithms with this fast-paced guideImplement tasks such as feature selection, search optimization, and cluster analysis using PythonSolve combinatorial problems, optimize functions, and enhance the performance of artificial intelligence applicationsBook DescriptionGenetic algorithms are a family of search, optimization, and learning algorithms inspired by the principles of natural evolution. By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence. After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications. By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains.What you will learnUnderstand how to use state-of-the-art Python tools to create genetic algorithm-based applicationsUse genetic algorithms to optimize functions and solve planning and scheduling problemsEnhance the performance of machine learning models and optimize deep learning network architectureApply genetic algorithms to reinforcement learning tasks using OpenAI GymExplore how images can be reconstructed using a set of semi-transparent shapesDiscover other bio-inspired techniques, such as genetic programming and particle swarm optimizationWho this book is forThis book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book.

DKK 480.00
1

Hands-On Data Analysis with Pandas - Stefanie Molin - Bog - Packt Publishing Limited - Plusbog.dk

Hands-On Data Analysis with Pandas - Stefanie Molin - Bog - Packt Publishing Limited - Plusbog.dk

Get to grips with pandas by working with real datasets and master data discovery, data manipulation, data preparation, and handling data for analytical tasksKey FeaturesPerform efficient data analysis and manipulation tasks using pandas 1.xApply pandas to different real-world domains with the help of step-by-step examplesMake the most of pandas as an effective data exploration toolBook DescriptionExtracting valuable business insights is no longer a ‘nice-to-have’, but an essential skill for anyone who handles data in their enterprise. Hands-On Data Analysis with Pandas is here to help beginners and those who are migrating their skills into data science get up to speed in no time. This book will show you how to analyze your data, get started with machine learning, and work effectively with the Python libraries often used for data science, such as pandas, NumPy, matplotlib, seaborn, and scikit-learn. Using real-world datasets, you will learn how to use the pandas library to perform data wrangling to reshape, clean, and aggregate your data. Then, you will learn how to conduct exploratory data analysis by calculating summary statistics and visualizing the data to find patterns. In the concluding chapters, you will explore some applications of anomaly detection, regression, clustering, and classification using scikit-learn to make predictions based on past data. This updated edition will equip you with the skills you need to use pandas 1.x to efficiently perform various data manipulation tasks, reliably reproduce analyses, and visualize your data for effective decision making – valuable knowledge that can be applied across multiple domains.What you will learnUnderstand how data analysts and scientists gather and analyze dataPerform data analysis and data wrangling using PythonCombine, group, and aggregate data from multiple sourcesCreate data visualizations with pandas, matplotlib, and seabornApply machine learning algorithms to identify patterns and make predictionsUse Python data science libraries to analyze real-world datasetsSolve common data representation and analysis problems using pandasBuild Python scripts, modules, and packages for reusable analysis codeWho this book is forThis book is for data science beginners, data analysts, and Python developers who want to explore each stage of data analysis and scientific computing using a wide range of datasets. Data scientists looking to implement pandas in their machine learning workflow will also find plenty of valuable know-how as they progress. You’ll find it easier to follow along with this book if you have a working knowledge of the Python programming language, but a Python crash-course tutorial is provided in the code bundle for anyone who needs a refresher.

DKK 564.00
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50 Algorithms Every Programmer Should Know - Imran Ahmad - Bog - Packt Publishing Limited - Plusbog.dk

50 Algorithms Every Programmer Should Know - Imran Ahmad - Bog - Packt Publishing Limited - Plusbog.dk

Delve into the realm of generative AI and large language models (LLMs) while exploring modern deep learning techniques, including LSTMs, GRUs, RNNs with new chapters included in this 50% new edition overhaulPurchase of the print or Kindle book includes a free eBook in PDF format. Key FeaturesFamiliarize yourself with advanced deep learning architecturesExplore newer topics, such as handling hidden bias in data and algorithm explainabilityGet to grips with different programming algorithms and choose the right data structures for their optimal implementationBook DescriptionThe ability to use algorithms to solve real-world problems is a must-have skill for any developer or programmer. This book will help you not only to develop the skills to select and use an algorithm to tackle problems in the real world but also to understand how it works. You'll start with an introduction to algorithms and discover various algorithm design techniques, before exploring how to implement different types of algorithms, with the help of practical examples. As you advance, you'll learn about linear programming, page ranking, and graphs, and will then work with machine learning algorithms to understand the math and logic behind them. Case studies will show you how to apply these algorithms optimally before you focus on deep learning algorithms and learn about different types of deep learning models along with their practical use. You will also learn about modern sequential models and their variants, algorithms, methodologies, and architectures that are used to implement Large Language Models (LLMs) such as ChatGPT. Finally, you'll become well versed in techniques that enable parallel processing, giving you the ability to use these algorithms for compute-intensive tasks. By the end of this programming book, you'll have become adept at solving real-world computational problems by using a wide range of algorithms.What you will learnDesign algorithms for solving complex problemsBecome familiar with neural networks and deep learning techniquesExplore existing data structures and algorithms found in Python librariesImplement graph algorithms for fraud detection using network analysisDelve into state-of-the-art algorithms for proficient Natural Language Processing illustrated with real-world examplesCreate a recommendation engine that suggests relevant movies to subscribersGrasp the concepts of sequential machine learning models and their foundational role in the development of cutting-edge LLMsWho this book is forThis computer science book is for programmers or developers who want to understand the use of algorithms for problem-solving and writing efficient code. Whether you are a beginner looking to learn the most used algorithms concisely or an experienced programmer looking to explore cutting-edge algorithms in data science, machine learning, and cryptography, you'll find this book useful. Python programming experience is a must, knowledge of data science will be helpful but not necessary.

DKK 491.00
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Hands-On Graph Neural Networks Using Python - Maxime Labonne - Bog - Packt Publishing Limited - Plusbog.dk

Hands-On Graph Neural Networks Using Python - Maxime Labonne - Bog - Packt Publishing Limited - Plusbog.dk

Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and appsPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesImplement -of-the-art graph neural architectures in PythonCreate your own graph datasets from tabular dataBuild powerful traffic forecasting, recommender systems, and anomaly detection applicationsBook DescriptionGraph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery. Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you’ll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps. By the end of this book, you’ll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more.What you will learnUnderstand the fundamental concepts of graph neural networksImplement graph neural networks using Python and PyTorch GeometricClassify nodes, graphs, and edges using millions of samplesPredict and generate realistic graph topologiesCombine heterogeneous sources to improve performanceForecast future events using topological informationApply graph neural networks to solve real-world problemsWho this book is forThis book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you’re new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.

DKK 460.00
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Architects of Intelligence - Martin Ford - Bog - Packt Publishing Limited - Plusbog.dk

Architects of Intelligence - Martin Ford - Bog - Packt Publishing Limited - Plusbog.dk

Book DescriptionHow will AI evolve and what major innovations are on the horizon? What will its impact be on the job market, economy, and society? What is the path toward human-level machine intelligence? What should we be concerned about as artificial intelligence advances?Architects of Intelligence contains a series of in-depth, one-to-one interviews where New York Times bestselling author, Martin Ford, uncovers the truth behind these questions from some of the brightest minds in the Artificial Intelligence community. Martin has wide-ranging conversations with twenty-three of the world's foremost researchers and entrepreneurs working in AI and robotics: Demis Hassabis (DeepMind), Ray Kurzweil (Google), Geoffrey Hinton (Univ. of Toronto and Google), Rodney Brooks (Rethink Robotics), Yann LeCun (Facebook) , Fei-Fei Li (Stanford and Google), Yoshua Bengio (Univ. of Montreal), Andrew Ng (AI Fund), Daphne Koller (Stanford), Stuart Russell (UC Berkeley), Nick Bostrom (Univ. of Oxford), Barbara Grosz (Harvard), David Ferrucci (Elemental Cognition), James Manyika (McKinsey), Judea Pearl (UCLA), Josh Tenenbaum (MIT), Rana el Kaliouby (Affectiva), Daniela Rus (MIT), Jeff Dean (Google), Cynthia Breazeal (MIT), Oren Etzioni (Allen Institute for AI), Gary Marcus (NYU), and Bryan Johnson (Kernel). Martin Ford is a prominent futurist, and author of Financial Times Business Book of the Year, Rise of the Robots. He speaks at conferences and companies around the world on what AI and automation might mean for the future.

DKK 364.00
1

Asynchronous Programming with C++ - Juan Antonio Rufes - Bog - Packt Publishing Limited - Plusbog.dk

Asynchronous Programming with C++ - Juan Antonio Rufes - Bog - Packt Publishing Limited - Plusbog.dk

Design and develop high-performance software solutions by using concurrent and asynchronous techniques provided by the most modern features in C++20 and C++23Key FeaturesLearn how to use modern C++ features, including futures, promises, async, and coroutines to build asynchronous solutionsDevelop cross-platform network and low-level I/O projects with Boost.AsioMaster optimization techniques by understanding how software adapts to machine hardwarePurchase of the print or Kindle book includes a free PDF eBookBook DescriptionAs hardware advancements continue to accelerate, bringing greater memory capacity and more CPU cores, software must evolve to adapt to efficiently use all available resources and reduce idle CPU cycles. In this book, two seasoned software engineers with about five decades of combined experience will teach you how to implement concurrent and asynchronous solutions in C++. You’ll gain a comprehensive understanding of parallel programming paradigms—covering concurrent, asynchronous, parallel, multithreading, reactive, and event-driven programming, as well as dataflows—and see how threads, processes, and services are related. Moving into the heart of concurrency, the authors will guide you in creating and managing threads and exploring C++’s thread-safety mechanisms, including mutual exclusion, atomic operations, semaphores, condition variables, latches, and barriers. With this solid foundation, you’ll focus on pure asynchronous programming, discovering futures, promises, the async function, and coroutines. The book takes you step by step through using Boost.Asio and Boost.Cobalt to develop network and low-level I/O solutions, proven performance and optimization techniques, and testing and debugging asynchronous software. By the end of this C++ book, you’ll be able to implement high-performance software using modern asynchronous C++ techniques.What you will learnExplore the different parallel paradigms and know when to apply themAcquire deep knowledge of thread management and safety mechanismsUnderstand asynchronous programming in C++, including coroutinesLeverage network asynchronous programming by using Boost.Asio and Boost.CobaltAdd proven performance and optimization techniques to your toolboxFind out how to test and debug asynchronous softwareWho this book is forThis book is for developers who have some experience using C++, regardless of their professional field. If you want to improve your C++ skills and learn how to develop high-performance software using the latest modern C++ features, this book is for you.

DKK 386.00
1

Cyber Security Kill Chain - Tactics and Strategies - Gourav Nagar - Bog - Packt Publishing Limited - Plusbog.dk

Cyber Security Kill Chain - Tactics and Strategies - Gourav Nagar - Bog - Packt Publishing Limited - Plusbog.dk

Understand the cyber kill chain framework and discover essential tactics and strategies to effectively prevent cyberattacksKey FeaturesExplore each stage of the cyberattack process using the cyber kill chain and track threat actor movementsLearn key components of threat intelligence and how they enhance the cyber kill chainApply practical examples and case studies for effective, real-time responses to cyber threatsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionGain a strategic edge in cybersecurity by mastering the systematic approach to identifying and responding to cyber threats through a detailed exploration of the cyber kill chain framework. This guide walks you through each stage of the attack, from reconnaissance and weaponization to exploitation, command and control (C2), and actions on objectives. Written by cybersecurity leaders Gourav Nagar, Director of Information Security at BILL Holdings, with prior experience at Uber and Apple, and Shreyas Kumar, Professor of Practice at Texas A&M, and former expert at Adobe and Oracle, this book helps enhance your cybersecurity posture. You’ll gain insight into the role of threat intelligence in boosting the cyber kill chain, explore the practical applications of the framework in real-world scenarios, and see how AI and machine learning are revolutionizing threat detection. You’ll also learn future-proofing strategies and get ready to counter sophisticated threats like supply chain attacks and living-off-the-land attacks, and the implications of quantum computing on cybersecurity. By the end of this book, you’ll have gained the strategic understanding and skills needed to protect your organization's digital infrastructure in the ever-evolving landscape of cybersecurity.What you will learnDiscover methods, tools, and best practices to counteract attackers at every stageLeverage the latest defensive measures to thwart command-and-control activitiesUnderstand weaponization and delivery techniques to improve threat recognitionImplement strategies to prevent unauthorized installations and strengthen securityEnhance threat prediction, detection, and automated response with AI and MLConvert threat intelligence into actionable strategies for enhancing cybersecurity defensesWho this book is forThis book is for cybersecurity professionals, IT administrators, network engineers, students, and business leaders who want to understand modern cyber threats and defense strategies. It’s also a valuable resource for decision-makers seeking insight into cybersecurity investments and strategic planning. With clear explanation of cybersecurity concepts suited to all levels of expertise, this book equips you to apply the cyber kill chain framework in real-world scenarios, covering key topics such as threat actors, social engineering, and infrastructure security.

DKK 405.00
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Cracking the Data Engineering Interview - Kedeisha Bryan - Bog - Packt Publishing Limited - Plusbog.dk

Cracking the Data Engineering Interview - Kedeisha Bryan - Bog - Packt Publishing Limited - Plusbog.dk

Get to grips with the fundamental concepts of data engineering, and solve mock interview questions while building a strong resume and a personal brand to attract the right employersKey FeaturesDevelop your own brand, projects, and portfolio with expert help to stand out in the interview roundGet a quick refresher on core data engineering topics, such as Python, SQL, ETL, and data modelingPractice with 50 mock questions on SQL, Python, and more to ace the behavioral and technical roundsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionPreparing for a data engineering interview can often get overwhelming due to the abundance of tools and technologies, leaving you struggling to prioritize which ones to focus on. This hands-on guide provides you with the essential foundational and advanced knowledge needed to simplify your learning journey. The book begins by helping you gain a clear understanding of the nature of data engineering and how it differs from organization to organization. As you progress through the chapters, you’ll receive expert advice, practical tips, and real-world insights on everything from creating a resume and cover letter to networking and negotiating your salary. The chapters also offer refresher training on data engineering essentials, including data modeling, database architecture, ETL processes, data warehousing, cloud computing, big data, and machine learning. As you advance, you’ll gain a holistic view by exploring continuous integration/continuous development (CI/CD), data security, and privacy. Finally, the book will help you practice case studies, mock interviews, as well as behavioral questions. By the end of this book, you will have a clear understanding of what is required to succeed in an interview for a data engineering role.What you will learnCreate maintainable and scalable code for unit testingUnderstand the fundamental concepts of core data engineering tasksPrepare with over 100 behavioral and technical interview questionsDiscover data engineer archetypes and how they can help you prepare for the interviewApply the essential concepts of Python and SQL in data engineeringBuild your personal brand to noticeably stand out as a candidateWho this book is forIf you’re an aspiring data engineer looking for guidance on how to land, prepare for, and excel in data engineering interviews, this book is for you. Familiarity with the fundamentals of data engineering, such as data modeling, cloud warehouses, programming (python and SQL), building data pipelines, scheduling your workflows (Airflow), and APIs, is a prerequisite.

DKK 291.00
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Azure Databricks Cookbook - Vinod Jaiswal - Bog - Packt Publishing Limited - Plusbog.dk

Azure Databricks Cookbook - Vinod Jaiswal - Bog - Packt Publishing Limited - Plusbog.dk

Get to grips with building and productionizing end-to-end big data solutions in Azure and learn best practices for working with large datasetsKey FeaturesIntegrate with Azure Synapse Analytics, Cosmos DB, and Azure HDInsight Kafka Cluster to scale and analyze your projects and build pipelinesUse Databricks SQL to run ad hoc queries on your data lake and create dashboardsProductionize a solution using CI/CD for deploying notebooks and Azure Databricks Service to various environmentsBook DescriptionAzure Databricks is a unified collaborative platform for performing scalable analytics in an interactive environment. The Azure Databricks Cookbook provides recipes to get hands-on with the analytics process, including ingesting data from various batch and streaming sources and building a modern data warehouse. The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You’ll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub. The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You'll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics. Later, you’ll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you'll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD). By the end of this Azure book, you'll be able to use Azure Databricks to streamline different processes involved in building data-driven apps.What you will learnRead and write data from and to various Azure resources and file formatsBuild a modern data warehouse with Delta Tables and Azure Synapse AnalyticsExplore jobs, stages, and tasks and see how Spark lazy evaluation worksHandle concurrent transactions and learn performance optimization in Delta tablesLearn Databricks SQL and create real-time dashboards in Databricks SQLIntegrate Azure DevOps for version control, deploying, and productionizing solutions with CI/CD pipelinesDiscover how to use RBAC and ACLs to restrict data accessBuild end-to-end data processing pipeline for near real-time data analyticsWho this book is forThis recipe-based book is for data scientists, data engineers, big data professionals, and machine learning engineers who want to perform data analytics on their applications. Prior experience of working with Apache Spark and Azure is necessary to get the most out of this book.

DKK 513.00
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C# 9 and .NET 5 – Modern Cross-Platform Development - Mark J. Price - Bog - Packt Publishing Limited - Plusbog.dk

C# 9 and .NET 5 – Modern Cross-Platform Development - Mark J. Price - Bog - Packt Publishing Limited - Plusbog.dk

Publisher’s Note: Microsoft stopped supporting .NET 5 in May 2022. The newer 8th edition of the book is available that covers .NET 8 (end-of-life November 2026) with C# 12 and EF Core 8. Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesExplore the newest additions to C# 9, the .NET 5 class library, Entity Framework Core and BlazorStrengthen your command of ASP.NET Core 5.0 and create professional websites and servicesBuild cross-platform apps for Windows, macOS, Linux, iOS, and AndroidBook DescriptionIn C# 9 and .NET 5 – Modern Cross-Platform Development, Fifth Edition, expert teacher Mark J. Price gives you everything you need to start programming C# applications. This latest edition uses the popular Visual Studio Code editor to work across all major operating systems. It is fully updated and expanded with a new chapter on the Microsoft Blazor framework. The book’s first part teaches the fundamentals of C#, including object-oriented programming and new C# 9 features such as top-level programs, target-typed new object instantiation, and immutable types using the record keyword. Part 2 covers the .NET APIs, for performing tasks like managing and querying data, monitoring and improving performance, and working with the file system, async streams, serialization, and encryption. Part 3 provides examples of cross-platform apps you can build and deploy, such as websites and services using ASP.NET Core or mobile apps using Xamarin.Forms. The best type of application for learning the C# language constructs and many of the .NET libraries is one that does not distract with unnecessary application code. For that reason, the C# and .NET topics covered in Chapters 1 to 13 feature console applications. In Chapters 14 to 20, having mastered the basics of the language and libraries, you will build practical applications using ASP.NET Core, Model-View-Controller (MVC), and Blazor. By the end of the book, you will have acquired the understanding and skills you need to use C# 9 and .NET 5 to create websites, services, and mobile apps.What you will learnBuild your own types with object-oriented programmingQuery and manipulate data using LINQBuild websites and services using ASP.NET Core 5Create intelligent apps using machine learningUse Entity Framework Core and work with relational databasesDiscover Windows app development using the Universal Windows Platform and XAMLBuild rich web experiences using the Blazor frameworkBuild mobile applications for iOS and Android using Xamarin.FormsWho this book is forThis book is best for C# and .NET beginners, or programmers who have worked with C# in the past but feel left behind by the changes in the past few years. This book doesn’t expect you to have any C# or .NET experience; however, you should have a general understanding of programming. Students and professionals with a science, technology, engineering, or mathematics (STEM) background can certainly benefit from this book.

DKK 657.00
1