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"C++ Programming Cookbook" stands out as a clear, concise, and powerful technical resource for programmers who want to master C++'s intricacies. C++ programmers face a wide variety of problems, and this carefully written book is a treasure trove of solutions and methods to those software development challenges.Each chapter is organized to help you get a good grasp of the language and everything it can do, from the basics of C++20 to more complex topics like sophisticated type manipulation and performance optimization. Through a series of carefully curated recipes, readers are invited on a journey of learning and competency. Starting with the fundamentals of creating a development environment and comprehending C++ syntax, the book progresses to cover more advanced subjects like concurrency, memory management, file I/O operations, object-oriented design concepts, functional programming, and more. This book focuses on the latest C++ features and aims to get programmers to use idiomatic C++ patterns and modern best practices."C++ Programming Cookbook" goes beyond being a mere collection of recipes; it serves as a manifesto for progressive software development practices and problem-solving. Readers are empowered to adapt and apply their learnings to new, unexplored situations because each recipe not only solves specific problems but also exposes fundamental ideas and methodologies. Writing code that is clean, efficient, and easy to maintain is a priority throughout the book, which aims to help readers develop a skill set that is applicable to more general programming problems.Key LearningsMake use of C++20 features to write more expressive, efficient, and modern C++ code effortlessly.Utilize template metaprogramming for compile-time calculations, enhancing code performance.Implement smart pointers for robust memory management without the usual complexity.Put object-oriented programming principles into use to design scalable and maintainable C++ applications.Explore advanced type manipulation techniques, ensuring type-safe and flexible code across applications.Harness concurrency and multithreading to build high-performance, responsive C++ software solutions.Optimize file I/O operations for seamless data handling in text and binary formats.Implement custom stream buffers for tailored data processing, boosting I/O efficiency.Navigate stream locales and facets for internationalizing your applications, reaching a global audience.Uncover efficient error and exception handling strategies to build reliable and error-free C++ program.Table of ContentGetting Started with C++20Deep Dive into FunctionsObject-Oriented Programming In ActionEffective use of STLExploring Advanced C++ FunctionalitiesEffective Error Handling and DebuggingConcurrency and MultithreadingPerformance and Memory ManagementAdvanced Type ManipulationFile I/O and Streams Operations

In this stimulating journey of Rust, you'll learn how to use the Rust programming language in conjunction with machine learning. It's not a full guide to learning machine learning with Rust. Instead, it's more of a journey that shows you what's possible when you use Rust to solve machine learning problems. Some people like Rust because it is quick and safe. This book shows how those qualities can help machine learning a lot.To begin, we will show you what Rust is and how it works. This is so that everyone, even those who are new to Rust, can follow along. Then, we look at some basic machine learning concepts, such as linear and logistic regression, and show how to use Rust's tools and libraries to make these ideas work.You will learn more complex techniques like decision trees, support vector machines, and how to work with data as we go along. It goes all the way up to neural networks and image recognition, and we show you how to use Rust for these types of tasks step by step. We use real-world examples, such as COVID data and the CIFAR-10 image set, to show how Rust works with issues that come up in the real world.This book is all about discovery and experimentation. To see what you can do with them, we use various Rust tools for machine learning. It's a fun way to see how Rust can be used in machine learning, and it will make you want to try new things and learn more on your own. This is only the beginning; there is so much more to uncover as you continue to explore machine learning with Rust. Key LearningsExploit Rust's efficiency and safety to construct fast machine learning models.Use Rust's ndarray crate for numerical computations to manipulate complex machine learning data.Find out how Rust's extensible machine learning framework, linfa, works across algorithms.Use Rust's precision and speed to construct linear and logistic regression.See how Rust crates simplify decision trees and random forests for prediction and categorization.Learn to implement and optimize probabilistic classifiers, SVMs and closest neighbor methods in Rust.Use Rust's computing power to study neural networks and CNNs for picture recognition and processing.Apply learnt strategies to COVID and CIFAR-10 datasets to address realistic problems and obtain insights. Table of ContentRust Basics for Machine LearningData Wrangling with RustLinear Regression by ExampleLogistic Regression for ClassificationDecision Trees in ActionMastering Random ForestsSupport Vector Machines in ActionSimplifying Naive Bayes and k-NNCrafting Neural Networks with Rust

This study-cum-exam guide will take you to every dimension in Terraform getting you prepared to confidently take HashiCorp's Terraform Certification exam. An extensive introduction to Terraform's architecture, covering its providers, resources, and state management features, sets the stage in the first chapter of the book. After you finish the first few chapters, you'll be behind the wheel, practicing for real-world situations with the help of several exercises.Using CloudOptic, a made-up but believable tech corporation, as an example, you will learn the ins and outs of module development and management. Starting with the basics and working its way up to advanced topics, this book walks you through every step of a Terraform project's lifetime.Still, it's only the start. State manipulation commands, resource targeting, and handling configuration drifts are just a few of the tough but vital topics covered thoroughly in this book. With examples and practical suggestions provided, you will learn how to work with variables, output values, dependencies, and state files.This book will teach you all you need to know about reusable modules and more, so you can create and integrate your own. The book's inclusion of exercises and real-world scenarios further increases its usefulness by providing ample opportunity to demonstrate mastery of the material. Every level of Terraform knowledge, from the most fundamental to the most complex, is covered thoroughly, preparing you to face any problem that comes your way.Key LearningsUnderstand the fundamentals, including providers, resources, and state files.An in-depth look at making reusable modules, dealing with dependencies, and using versioning.Applicable, real-world context learning through the made-up corporation CloudOptic.Acquire knowledge of commands for manipulating states, handling drifts, and version control.Gain a grasp of the most important metrics and best practices for scaling Terraform systems.Proficiency in maintaining the Terraform codebase for future versions and ensuring compatibility.Instructions for setting up a connection between Terraform and the CDKTF.Code in accordance with industry standards to make Terraform scripts more dependable and cleaner.Prep for HashiCorp's Terraform Certification with tough quizzes and exercises.Table of ContentIaC and TerraformUnderstanding Terraform BasicsTerraform WorkflowState Management and BackendAdvanced Configuration TechniquesTerraform ModulesTerraform Import and MaintenanceBest Practices and Design PatternsAudienceAnyone with a basic understanding of HashiCorp Terraform and a background in cloud, IT operations, devops, or software development can apply for this certification. Prior knowledge of on-premise and cloud architectural is preferred although not mandatory.

The Go Programming Cookbook ensures Go programmers may confidently and effectively navigate the complex software development landscape. Both newcomers to the world of Go programming and seasoned professionals looking to sharpen their skills will find something of interest in this carefully written book.This cookbook covers a wide range of topics, from the basics of Go syntax and core ideas to more advanced subjects like concurrency, networking, and microservices design. Building a Go development environment, learning the fundamentals of Go constructs, and mastering the art of Go Module dependency management are all covered in the first few chapters, which prepare readers for what's to come. To guarantee that readers can construct strong and maintainable programs, the next sections cover error handling, unit testing, and database interfaces. The book really comes into its own when it comes to its in-depth examination of network programming; it covers topics like building HTTP clients, implementing FTP and SSH clients, and using WebSockets for real-time communication.To prepare developers to create safe, networked applications in a world where everything is always connected, it teaches them how to establish secure TLS/SSL communications, which is a fundamental component of security. In addition to providing practical solutions, the recipes educate readers on the concurrency model and design principles of Go, which helps them build an intuitive understanding of high-performance software development.Key LearningsGet a solid grounding in programming by learning the syntax and concepts of Go.Explore concurrency with Goroutines and Channels to maximize Go's possibilities.Effortlessly handle intricate network programming jobs involving WebSockets and HTTP clients.Learn TLS/SSL inside and out to make your applications secure for transmitted sensitive information.Effectively manage data by integrating with SQL and NoSQL databases.Use Go Modules to have easier dependency management and build reproducibility.Make your Go code clean and easy to maintain by following design patterns and other best practices.Get the most out of your system by learning about memory management, benchmarking, and profiling.Create web servers and microservices from the ground up that are scalable and perform exceptionally well.Become an expert Go problem solver by learning practical answers to real-world issues.Table of ContentSetting up and Exploring GoAdvanced Go Features and TechniquesFile Handling and Data Processing in GoBuilding and Managing Go APIsImplementing RPC and gRPC Services in GoWeb Services and Automation using GoBuilding Microservices Architecture using GoStrengthening Database InteractionsEnhancing Performance and Best Practices in GoNetworking and Protocol Handling

This capsule book is designed to provide DevOps teams, Networking Professionals, and Cloud Enthusiasts with the practical knowledge and skills required to set up and operate a robust service mesh with Linkerd. The book begins by demystifying the concept of service meshes, building a solid basis with an analysis of their evolution, key concepts, and the issues they face in modern cloud-native systems. It digs into Linkerd's architecture, explaining its components, features, and the seamless orchestration of microservices communication that it enables. As readers progress through the chapters, they are taken step by step through the installation and configuration of Linkerd. The book focuses on actual implementation, guiding readers through imperative and declarative methods to ensure a complete comprehension of the setup process.The following chapters cover advanced subjects such as safeguarding interservice communications, configuring secure multi-cluster links, and implementing zero-trust authorization schemes in Kubernetes clusters. Topics includes how to organize services within Linkerd, manage error handling, retries, and timeouts, and implement effective multi-cluster communication and rollout strategies. A key chapter is about Rust programming, emphasizing its importance in developing efficient and secure micro proxies. Readers learn how to construct, integrate, and optimize these proxies to improve their service mesh deployment.The book's conclusion prepares readers to work around progressive delivery, high availability, and integration with a variety of cloud settings and tools. This book serves as a complete guide, transforming its readers into skilled architects of Linkerd-based service mesh solutions, prepared to face the dynamic challenges of modern cloud-native infrastructures.Key LearningsGrasp the essentials of service mesh technology, focusing on Linkerd's transformative role in it.Uncover the architecture of Linkerd, understanding its components and operational dynamics.Master the installation and configuration of Linkerd, ensuring a seamless setup process.Learn to secure interservice communication, enhancing the reliability and safety of your network.Explore multi-cluster communication strategies, enabling robust and efficient service interactions.Delve into Rust programming for building high-performance, secure micro proxies in xii Linkerd.Gain insights into advanced traffic management using Linkerd for optimal service routing.Navigate the intricacies of progressive delivery for deploying updates with minimal user impact.Discover the power of high availability in service meshes, ensuring uninterrupted service.Develop proficiency in integrating and optimizing linkerd2-proxy, harnessing its full potential.Table of ContentIntroduction to Service MeshLinkerd Architecture: Up and RunningInstalling and Configuring LinkerdSecuring Communication with LinkerdAdvanced Traffic ManagementMulti-Cluster Communication and RolloutsProgressive Delivery and Ingress IntegrationBuilding Micro Proxies with Rust

"Neural Networks with Python" serves as an introductory guide for those taking their first steps into neural network development with Python. It's tailored to assist beginners in understanding the foundational elements of neural networks and to provide them with the confidence to delve deeper into this intriguing area of machine learning.In this book, readers will embark on a learning journey, starting from the very basics of Python programming, progressing through essential concepts, and gradually building up to more complex neural network architectures. The book simplifies the learning process by using relatable examples and datasets, making the concepts accessible to everyone. You will be introduced to various neural network architectures such as Feedforward, Convolutional, and Recurrent Neural Networks, among others. Each type is explained in a clear and concise manner, with practical examples to illustrate their applications. The book emphasizes the real-world applications and practical aspects of neural network development, rather than just theoretical knowledge.Readers will also find guidance on how to troubleshoot and refine their neural network models. The goal is to equip you with a solid understanding of how to create efficient and effective neural networks, while also being mindful of the common challenges that may arise.By the end of your journey with this book, you will have a foundational understanding of neural networks within the Python ecosystem and be prepared to apply this knowledge to real-world scenarios. "Neural Networks with Python" aims to be your stepping stone into the vast world of machine learning, empowering you to build upon this knowledge and explore more advanced topics in the future.Key LearningsMaster Python for machine learning, from setup to complex models.Gain flexibility with diverse neural network architectures for various problems.Hands-on experience in building, training, and fine-tuning neural networks.Learn strategic approaches for troubleshooting and optimizing neural models.Grasp advanced topics like autoencoders, capsule networks, and attention mechanisms.Acquire skills in crucial data preprocessing and augmentation techniques.Understand and apply optimization techniques and hyperparameter tuning.Implement an end-to-end machine learning project, from data to deployment.Table of ContentPython, TensorFlow, and your First Neural NetworkDeep Dive into Feedforward NetworksConvolutional Networks for Visual TasksRecurrent Networks for Sequence DataData Generation with GANsTransformers for Complex TasksAutoencoders for Data Compression and GenerationCapsule Networks

This book aspires young graduates and programmers to become AI engineers and enter the world of artificial intelligence by combining powerful Python programming with artificial intelligence. Beginning with the fundamentals of Python programming, the book gradually progresses to machine learning, where readers learn to implement Python in developing predictive models.The book provides a clear and accessible explanation of machine learning, incorporating practical examples and exercises that strengthen understanding. We go deep into deep learning, another vital component of AI. Readers gain a thorough understanding of how Python's frameworks and libraries can be used to create sophisticated neural networks and algorithms, which are required for tasks such as image and speech recognition. Natural Language Processing is also covered in the book, with fundamental concepts and techniques for interpreting and generating human-like language covered.The book's focus on computer vision and reinforcement learning is distinctive, presenting these cutting-edge AI fields in an approachable manner. Readers will learn how to use Python's intuitive programming paradigm to create systems that interpret visual data and make intelligent decisions based on environmental interactions. The book focuses on ethical AI development and responsible programming, emphasizing the importance of developing AI that is fair, transparent, and accountable.Each chapter is designed to improve learning by including practical examples, case studies, and exercises that provide hands-on experience. This book is an excellent starting point for anyone interested in becoming an AI engineer, providing the necessary foundational knowledge and skills to delve into the fascinating world of artificial intelligence.Key LearningsExplore Python basics and AI integration for real-world application and career advancement.Experience the power of Python in AI with practical machine learning techniques.Practice Python's deep learning tools for innovative AI solution development.Dive into NLP with Python to revolutionize data interpretation and communication strategies.Simple yet practical understanding of reinforcement learning for strategic AI decision making.Uncover ethical AI development and frameworks, and concepts of responsible and trustworthy AI.Harness Python's capabilities for creating AI applications with a focus on fairness and bias.Table of ContentIntroduction to Artificial IntelligencePython for AIData as Fuel for AIMachine Learning FoundationEssentials of Deep LearningNLP and Computer VisionHands-on Reinforcement LearningEthics to AI

A practical handbook, "PowerShell Troubleshooting Guide" is designed to help PowerShell enthusiasts improve their skills and make them more effective in real-world applications. Starting with basic scripting and progressing to comprehensive system expertise, the book explores the immense possibilities of PowerShell.Beginning with fundamental ideas, readers are exposed to the heart of PowerShell, including its architecture, command structures, and scripting intricacies. Each chapter delves into a specific theme, such as troubleshooting approaches, advanced debugging, loop controls, and robust error-handling systems, ensuring that the reader is well-prepared to face any obstacles that may arise.One of the book's strongest points is its emphasis on hands-on learning. It gives you hands-on experience automating complex system and Windows administrative operations while demystifying the processes involved. Readers will learn how to establish secure communication channels, manage remote sessions, and transfer files to faraway systems with the help of realistic examples and clear explanations. Combining this remote knowledge with an in-depth examination of debugging, experts will be able to fix any problems with their automation solutions quickly and easily.Most importantly, this book takes readers on a trip that will elevate them from PowerShell user to PowerShell maestro, allowing them to solve all of their administrative problems in a way that is streamlined, efficient, and imaginative.Key LearningsGrasp core PowerShell concepts, ensuring a robust base for advanced operations.Learn to craft effective scripts, optimizing automation tasks.Dive into managing networks remotely, ensuring seamless operations.Acquire skills to troubleshoot scripts, ensuring error-free automation.Understand Windows Management Instrumentation, linking it with PowerShell.Prioritize secure scripting and master remote sessions, ensuring system integrity, connectivity and control.Adopt industry-standard best practices for PowerShell.Table of ContentIntroduction to PowerShell TroubleshootingUnderstanding PowerShell Command-Line ToolsWorking with PowerShell ISEPowerShell ModulesScripting in PowerShellUnderstanding Automatic VariablesDebugging TechniquesWorking with While LoopsManaging Windows SystemsRemote System ManagementTarget ReadersThis book is intended for the whole PowerShell community and everyone who is required to work with PowerShell in any capacity. This book assumes no prior knowledge and will quickly transform you into a competent, talented, solution-focused, and smart powershell practitioner. Following along this book requires only basic understanding of scripting.

Web Programming with Go is a must-have book for software professionals and web developers looking to harness Go's potential for creating excellent web apps. The extensive knowledge imparted by this book about Go's powerful libraries and packages is crucial for building scalable and efficient web platforms.The book's core focus is on exploring deeply into the complexities of web development through the perspective of Go. The first step in building any web application is to review the fundamentals, which will serve as a foundation for the rest of the learning experience. As you explore deeper, you'll learn about the diverse ecosystem that supports Go. This book covers a wide range of issues, including the complexities of routing mechanics, HTTP requests, and Go's concurrency model, all with the goal of improving web efficiency.The book uses the example of a "bookstore app" throughout the book, providing you with a practical touchpoint on every possible learning. With this iterative example, you can follow the development of a simple web app from its infancy all the way to a fully featured, robust platform. The later sections of the book focus on performance evaluation and debugging, which are frequently overlooked in other Go books.This book takes you from the fundamentals of web development to the depths of Go, culminating in a strong online application designed to meet today's digital issues. This book promises to be your compass in the vast ocean of web development, whether you're an experienced developer or an enthusiast just getting started with Go.Key LearningsMaster Go's efficient syntax and streamline coding with better performance.Build robust web applications from scratch, ensuring scalability and responsiveness.Seamlessly integrate APIs, enhancing app functionality and user experience.Harness Go's concurrency, boosting app speed and multitasking capabilities.Optimize data storage and retrieval with Go, ensuring data integrity and speed.Develop resilient apps by mastering error detection and troubleshooting in Go.Implement user sessions, enhancing user experience and data security.Ensure app reliability through Go's comprehensive testing and debugging techniques.Utilize Go tools for real-time performance tracking, ensuring optimal user experiences.Safeguard user data and interactions through Go's top-tier security practices.Table of ContentIntroduction to Web Development in GoStructuring Go Web ApplicationHandling HTTP Requests and RoutingTemplating and Rendering ContentInteraction with DatabasesConcurrency in GoSessions, Authentication and AuthorizationFrontend and Backend CommunicationTesting and Debugging

To help aspiring IT professionals succeed in the automation field, "Learning Ansible" provides an in-depth primer that covers all the essential skills. The book is an excellent resource that will help you become an expert IT automation specialist and Ansible Developer.Starting with an introduction to Ansible, the journey will show how crucial it is in today's IT settings. The first few chapters cover the essentials, diving into topics like configuration management, automation, integration, and orchestration. The authors make sure that readers can confidently troubleshoot issues by thoroughly examining network, performance, and security errors and providing advanced debugging techniques. Practical uses of Ansible in system administration, user and group management, and managing common and complex errors are explored throughout the book. Setup of network devices, software upgrade automation, and fixing recurring network problems are all covered in detail, along with example programs.Next, we'll take a closer look at DevOps, with a focus on how Ansible has impacted CI/CD pipelines. The course teaches readers how to integrate Ansible with popular tools and cloud providers like AWS and Azure through and various tools such as Jenkins, Docker, and Kubernetes.Additionally, integration is extended to databases such as MySQL and MongoDB, as well as monitoring tools such as Nagios and Prometheus. By incorporating practical exercises throughout, the book guarantees that its readers will grasp the concepts and be able to put them to use in actual situations.Key LearningsA thorough introduction to Ansible, laying the groundwork for mastering IT automation techniques.In-depth examination of system administration, with emphasis on improving user and group management skills.Detailed instructions for debugging in Ansible, enhancing problem-solving and troubleshooting abilities.Practical demonstrations of network device configuration, aimed at improving network management skills.Hands-on Ansible integration with Jenkins, Docker, and Kubernetes, enhancing CI/CD capabilities.Integration with cloud providers such as AWS and Azure to strengthen cloud management skills.Techniques for automating workstation setup and software upgrades, with the goal of increasing automation efficiency.Integration with monitoring and logging tools, promoting best practices in system monitoring.Using Ansible to build a full-stack application setup and gain end-to-end automation expertise.Consistent application of web server sample program, which makes learning practical and relatable.Table of ContentIntroduction to AnsiblePlaybooks and TasksWorking with ModulesRoles, Files and TemplatingManaging Systems with AnsibleAnsible in NetworkingAnsible for DevOpsTroubleshooting AnsibleIntegrating Ansible with Other Tools

Starting a PyTorch Developer and Deep Learning Engineer career? Check out this 'PyTorch Cookbook,' a comprehensive guide with essential recipes and solutions for PyTorch and the ecosystem. The book covers PyTorch deep learning development from beginner to expert in well-written chapters.The book simplifies neural networks, training, optimization, and deployment strategies chapter by chapter. The first part covers PyTorch basics, data preprocessing, tokenization, and vocabulary. Next, it builds CNN, RNN, Attentional Layers, and Graph Neural Networks. The book emphasizes distributed training, scalability, and multi-GPU training for real-world scenarios. Practical embedded systems, mobile development, and model compression solutions illuminate on-device AI applications. However, the book goes beyond code and algorithms. It also offers hands-on troubleshooting and debugging for end-to-end deep learning development. 'PyTorch Cookbook' covers data collection to deployment errors and provides detailed solutions to overcome them.This book integrates PyTorch with ONNX Runtime, PySyft, Pyro, Deep Graph Library (DGL), Fastai, and Ignite, showing you how to use them for your projects. This book covers real-time inferencing, cluster training, model serving, and cross-platform compatibility. You'll learn to code deep learning architectures, work with neural networks, and manage deep learning development stages. 'PyTorch Cookbook' is a complete manual that will help you become a confident PyTorch developer and a smart Deep Learning engineer. Its clear examples and practical advice make it a must-read for anyone looking to use PyTorch and advance in deep learning.Key LearningsComprehensive introduction to PyTorch, equipping readers with foundational skills for deep learning.Practical demonstrations of various neural networks, enhancing understanding through hands-on practice.Exploration of Graph Neural Networks (GNN), opening doors to cutting-edge research fields.In-depth insight into PyTorch tools and libraries, expanding capabilities beyond core functions.Step-by-step guidance on distributed training, enabling scalable deep learning and AI projects.Real-world application insights, bridging the gap between theoretical knowledge and practical execution.Focus on mobile and embedded development with PyTorch, leading to on-device AI.Emphasis on error handling and troubleshooting, preparing readers for real-world challenges.Advanced topics like real-time inferencing and model compression, providing future ready skill.Table of ContentIntroduction to PyTorch 2.0Deep Learning Building BlocksConvolutional Neural NetworksRecurrent Neural NetworksNatural Language ProcessingGraph Neural Networks (GNNs)Working with Popular PyTorch ToolsDistributed Training and ScalabilityMobile and Embedded Development

Unlock the Power of Parallel Python with Dask: A Perfect Learning Guide for Aspiring Data ScientistsDask has revolutionized parallel computing for Python, empowering data scientists to accelerate their workflows. This comprehensive guide unravels the intricacies of Dask to help you harness its capabilities for machine learning and data analysis.Across 10 chapters, you'll master Dask's fundamentals, architecture, and integration with Python's scientific computing ecosystem. Step-by-step tutorials demonstrate parallel mapping, task scheduling, and leveraging Dask arrays for NumPy workloads. You'll discover how Dask seamlessly scales Pandas, Scikit-Learn, PyTorch, and other libraries for large datasets.Dedicated chapters explore scaling regression, classification, hyperparameter tuning, feature engineering, and more with clear examples. You'll also learn to tap into the power of GPUs with Dask, RAPIDS, and Google JAX for orders of magnitude speedups.This book places special emphasis on practical use cases related to scalability and distributed computing. You'll learn Dask patterns for cluster computing, managing resources efficiently, and robust data pipelines. The advanced chapters on DaskML and deep learning showcase how to build scalable models with PyTorch and TensorFlow.With this book, you'll gain practical skills to:Accelerate Python workloads with parallel mapping and task schedulingSpeed up NumPy, Pandas, Scikit-Learn, PyTorch, and other librariesBuild scalable machine learning pipelines for large datasetsLeverage GPUs efficiently via Dask, RAPIDS and JAXManage Dask clusters and workflows for distributed computingStreamline deep learning models with DaskML and DL frameworksPacked with hands-on examples and expert insights, this book provides the complete toolkit to harness Dask's capabilities. It will empower Python programmers, data scientists, and machine learning engineers to achieve faster workflows and operationalize parallel computing.Table of ContentIntroduction to DaskDask FundamentalsBatch Data Parallel Processing with DaskDistributed Systems and DaskAdvanced Dask: APIs and Building BlocksDask with PandasDask with Scikit-learnDask and PyTorchDask with GPUsScaling Machine Learning Projects with Dask

Unleash the Speed: Master WebAssembly and Take Your Web Apps to the Next LevelThrough practical examples, you'll learn how WebAssembly can transform C++, Rust, and other languages into lightning-fast web apps. Step-by-step, you'll master techniques to integrate WebAssembly modules into JavaScript projects for seamless communication.You'll discover strategies to optimize code, leverage multi-threading, and apply cutting-edge methods like lazy loading. We also tackle memory management and dive into profiling, benchmarking, and browser tools to push performance to the limits.This isn't just essentials of webassembly - it's a roadmap to learn intricacies, best practices, and solutions to common pitfalls. The goal is to equip you with the expertise to develop insanely fast applications. The future is native-level performance on the web. Join the revolution and skill up with this guide to mastering WebAssembly. Your web apps will never be the same!Key LearningsGrasp WASMs core concepts, bridging web and native apps, boosting performance seamlessly.Dive into tools like Emscripten and Binaryen, facilitating efficient cross language compilations to Wasm.Learn techniques to convert C++, Python, and Java apps, unlocking web application potential.Seamlessly integrate JavaScript and WebAssembly, ensuring smooth inter-module communications.Harness strategies for fine-tuning code, guaranteeing peak application performance.Explore multi-threading, leveraging simultaneous operations, accelerating execution times.Implement lazy loading and code splitting, optimizing web app responsiveness and load times.Dive deep into efficient memory usage, ensuring resourceful application execution.Proficiently use browser developer tools, dissecting and enhancing application performance.Enhance user experience with effective caching, ensuring rapid web application accessibilityPrerequisitesThis book is for every programmer aspiring to put their all applications onto web browser with the same speed, efficiency and product performance. Knowing fundamentals of any programming language and basics of web app development is sufficient to tear out the book wholly.

Designed with both beginners and professionals in mind, the book is meticulously structured to cover a broad spectrum of concepts, applications, and hands-on practices that form the core of the TensorFlow Developer Certificate exam. Starting with foundational concepts, the book guides you through the fundamental aspects of TensorFlow, Machine Learning algorithms, and Deep Learning models.The initial chapters focus on data preprocessing, exploratory analysis, and essential tools required for building robust models. The book then delves into Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and advanced neural network techniques such as GANs and Transformer Architecture. Emphasizing practical application, each chapter is peppered with detailed explanations, code snippets, and real-world examples, allowing you to apply the concepts in various domains such as text classification, sentiment analysis, object detection, and more.A distinctive feature of the book is its focus on various optimization and regularization techniques that enhance model performance. As the book progresses, it navigates through the complexities of deploying TensorFlow models into production. It includes exhaustive sections on TensorFlow Serving, Kubernetes Cluster, and edge computing with TensorFlow Lite. The book provides practical insights into monitoring, updating, and handling possible errors in production, ensuring a smooth transition from development to deployment.The final chapters are devoted to preparing you for the TensorFlow Developer Certificate exam. From strategies, tips, and coding challenges to a summary of the entire learning journey, these sections serve as a robust toolkit for exam readiness. With hints and solutions provided for challenges, you can assess your knowledge and fine-tune your problem solving skills. In essence, this book is more than a mere certification guide; it's a complete roadmap to mastering TensorFlow. It aligns perfectly with the objectives of the TensorFlow Developer Certificate exam, ensuring that you are not only well-versed in the theoretical aspects but are also skilled in practical applications.Key LearningsComprehensive guide to TensorFlow, covering fundamentals to advanced topics, aiding seamless learning.Alignment with TensorFlow Developer Certificate exam, providing targeted preparation and confidence.In-depth exploration of neural networks, enhancing understanding of model architecture and function.Hands-on examples throughout, ensuring practical understanding and immediate applicability of concepts.Detailed insights into model optimization, including regularization, boosting model performance.Extensive focus on deployment, from TensorFlow Serving to Kubernetes, for real-world applications.Exploration of innovative technologies like BiLSTM, attention mechanisms, Transformers, fostering creativity.Step-by-step coding challenges, enhancing problem-solving skills, mirroring real-world scenarios.Coverage of potential errors in deployment, offering practical solutions, ensuring robust applications.Continual emphasis on practical, applicable knowledge, making it suitable for all levelsTable of ContentsIntroduction to Machine Learning and TensorFlow 2.xUp and Running with Neural NetworksBuilding Basic Machine Learning ModelsImage Recognition with CNNObject Detection AlgorithmsText Recognition and Natural Language ProcessingStrategies to Prevent Overfitting & UnderfittingAdvanced Neural Networks for NLPProductionizing TensorFlow ModelsPreparing for TensorFlow Developer Certificate Exam

"Practical C++ Backend Programming" is a comprehensive walkthrough that provides readers with the necessary tools and knowledge to become proficient C++ backend developers. With a strong focus on real-world application and practical implementation, this book takes readers on a journey through the multifaceted landscape of backend development, making it an essential resource for any aspiring or current backend developer.Starting with the basics, the book introduces C++, providing a solid foundation in the language, its structure, and core concepts with regards to backend programming. From there, readers dive into the more complex elements of backend development. Through our engaging sections, use-cases and sample examples, readers are introduced to advanced topics such as concurrent programming, exploring threading and multiprocessing to handle intensive computational tasks, thus laying the groundwork for scalable applications.This book offers an in-depth look into APIs, specifically gRPC, along with caching strategies, and database management, using MongoDB as a NoSQL database of choice. All the while, readers will learn to implement these technologies in a practical context, building a blog application from scratch, thereby bridging the gap between theory and practical implementation. An entire section is dedicated to securing applications, wherein the book teaches authentication, authorization, and database security, and demonstrates how to implement these measures in the blog application.Another utmost important part of this book is to cover testing strategies, teaching the reader how to employ Google Test (gtest) to create robust and fail-proof backend solutions. Finally, the journey culminates in a step-by-step walkthrough to deploying applications on AWS, ensuring the reader is equipped with the necessary skills to take their applications live.Key LearningsDetailed overview of C++ programming, catering to both beginners and experienced coders.Practical exploration of concurrent programming for scalable and efficient application design.Comprehensive understanding of API usage, specifically using the gRPC framework.Deep dive into MongoDB for effective NoSQL database management and operations.Thorough walkthrough to implementing caching strategies for performance optimization.Strategic use of Nginx for handling web server needs including load balancing.Hands-on guide to implementing security measures for databases, APIs, and web servers.Instruction on employing Google Test for robust application testing and debugging.Step-by-step guidance for deploying applications on AWS, ensuring real-world readiness.Practical application of concepts via building and refining a blog application.Table of ContentIntroduction to Backend DevelopmentC++ Refresher and EssentialsDeep Dive into AlgorithmsMastering Version Control - Git and GitHubManaging Database Operations with MongoDBCrafting Rest APIs with gRPCDealing with Client-side and Server-side CachingManaging Web Servers with NginxTesting Your C++ BackendSecuring Your C++ BackendDeploying Your ApplicationAudienceThis book is appropriate for readers with some background in C++ and nothing about back-end development. It's great for those just getting their feet wet in back-end, as well as seasoned pros looking to hone their craft and learn something new. Whether you're a student, or professional this book will teach you everything you need to know to master the art of C++ back-end development.

"Terraform for Developers" provides a comprehensive guide to mastering infrastructure as code with Terraform, intended for both beginners and experienced practitioners. The book starts by introducing foundational Terraform concepts like its architecture, setup on various cloud platforms, and the syntax of its configuration language HCL. This grounds the reader in core skills needed to author infrastructure code.It then explores the full lifecycle of a Terraform project from state management, provisioning, networking, troubleshooting errors, and testing practices. Each phase is explained through practical examples relevant to real-world usage. Advanced skills like performance optimization, CI/CD integration, and modules are covered to take the reader from basic to expert.The book finishes with sophisticated use cases like Kubernetes infrastructure automation and secrets management with HashiCorp Vault with elementary level demonstrations. The end result is a 360-degree education in Terraform - from installation to advanced usage across public clouds. Readers gain both theoretical foundations and hands-on skills transferred through practical examples. The book takes a holistic approach that equips practitioners to leverage Terraform in production environments.[Note: Readers are advised not to misguide this book as a mastering book but does covers every possible task Terraform can perform.]Key LearningsComprehensive introduction to Terraform's architecture, syntax, and semantics.Practical examples to understand Infrastructure as Code (IaC) concept.Detailed guidance on setting up Terraform in diverse environments.Exploring Terraform's lifecycle, from initialization to resource destruction.Insightful exploration of error management and troubleshooting techniques.In-depth discussion on network management using Terraform for real-world scenarios.Advanced topics coverage like Kubernetes resources and dynamic secrets.Detailed explanation of Terraform's integration with CI/CD pipelines.Extensive study of Terraform testing, including unit, integration, and compliance tests.Knowledge on module creation and version control for efficient code reuse.Table of ContentIntroduction to Terraform and Infrastructure as Code (IaC)Getting Started with TerraformEssentials of Terraform Configuration Language (HCL)Terraform Modules and Reusable InfrastructureTerraform State ManagementProvisioners and Provisioning ResourcesWorking with SecretsAdministering Networks with TerraformAdvanced Topics in TerraformAdministering Terraform As ExpertAudienceThis book is designed for software professionals, system administrators, DevOps engineers, and cloud architects who aim to master Infrastructure as Code (IaC) using Terraform. Basic knowledge of cloud computing and some experience with coding will aid in comprehending the book's content.

This book is a comprehensive guide to understanding and utilizing PyTorch 2.0 for deep learning applications. It starts with an introduction to PyTorch, its various advantages over other deep learning frameworks, and its blend with CUDA for GPU acceleration. We delve into the heart of PyTorch - tensors, learning their different types, properties, and operations. Through step-by-step examples, the reader learns to perform basic arithmetic operations on tensors, manipulate them, and understand errors related to tensor shapes.A substantial portion of the book is dedicated to illustrating how to build simple PyTorch models. This includes uploading and preparing datasets, defining the architecture, training, and predicting. It provides hands-on exercises with a real-world dataset. The book then dives into exploring PyTorch's nn module and gives a detailed comparison of different types of networks like Feedforward, RNN, GRU, CNN, and their combination.Further, the book delves into understanding the training process and PyTorch's optim module. It explores the overview of optimization algorithms like Gradient Descent, SGD, Mini-batch Gradient Descent, Momentum, Adagrad, and Adam. A separate chapter focuses on advanced concepts in PyTorch 2.0, like model serialization, optimization, distributed training, and PyTorch Quantization API.In the final chapters, the book discusses the differences between TensorFlow 2.0 and PyTorch 2.0 and the step-by-step process of migrating a TensorFlow model to PyTorch 2.0 using ONNX. It provides an overview of common issues encountered during this process and how to resolve them.Key LearningsA comprehensive introduction to PyTorch and CUDA for deep learning.Detailed understanding and operations on PyTorch tensors.Step-by-step guide to building simple PyTorch models.Insight into PyTorch's nn module and comparison of various network types.Overview of the training process and exploration of PyTorch's optim module.Understanding advanced concepts in PyTorch like model serialization and optimization.Knowledge of distributed training in PyTorch.Practical guide to using PyTorch's Quantization API.Differences between TensorFlow 2.0 and PyTorch 2.0.Guidance on migrating TensorFlow models to PyTorch using ONNX.Table of ContentIntroduction to Pytorch 2.0 and CUDA 11.8Getting Started with TensorsAdvanced Tensors OperationsBuilding Neural Networks with PyTorch 2.0Training Neural Networks in PyTorch 2.0PyTorch 2.0 AdvancedMigrating from TensorFlow to PyTorch 2.0End-to-End PyTorch Regression ModelAudienceA perfect and skillful book for every machine learning engineer, data scientist, AI engineer and data researcher who are passionately looking towards drawing actionable intelligence using PyTorch 2.0. Knowing Python and the basics of deep learning is all you need to sail through this book.

"Google JAX Essentials" is a comprehensive guide designed for machine learning and deep learning professionals aiming to leverage the power and capabilities of Google's JAX library in their projects. Over the course of eight chapters, this book takes the reader from understanding the challenges of deep learning and numerical computations in the existing frameworks to the essentials of Google JAX, its functionalities, and how to leverage it in real-world machine learning and deep learning projects.The book starts by emphasizing the importance of numerical computing in ML and DL, demonstrating the limitations of standard libraries like NumPy, and introducing the solution offered by JAX. It then guides the reader through the installation of JAX on different computing environments like CPUs, GPUs, and TPUs, and its integration into existing ML and DL projects. The book details the advanced numerical operations and unique features of JAX, including JIT compilation, automatic differentiation, batched operations, and custom gradients. It illustrates how these features can be employed to write code that is both simpler and faster.The book also delves into parallel computation, the effective use of the vmap function, and the use of pmap for distributed computing. Lastly, the reader is walked through the practical application of JAX in training different deep learning models, including RNNs, CNNs, and Bayesian models, with an additional focus on performance-tuning strategies for JAX applications.Key LearningsMastering the installation and configuration of JAX on various computing environments.Understanding the intricacies of JAX's advanced numerical operations.Harnessing the power of JIT compilation in JAX for accelerated computations.Implementing batched operations using the vmap function for efficient processing.Leveraging automatic differentiation and custom gradients in JAX.Proficiency in using the pmap function for distributed computing in JAX.Training different types of deep learning models using JAX.Applying performance tuning strategies to maximize JAX application efficiency.Integrating JAX into existing machine learning and deep learning projects.Complementing the official JAX documentation with practical, real-world applications.Table of ContentNecessity for Google JAXUnravelling JAXSetting up JAX for Machine Learning and Deep LearningJAX for Numerical ComputingDiving Deeper into Auto Differentiation and GradientsEfficient Batch Processing with JAXPower of Parallel Computing with JAXTraining Neural Networks with JAXAudienceThis is must read for machine learning and deep learning professionals to be skilled with the most innovative deep learning library. Knowing Python and experience with machine learning is sufficient is desired to begin with this book

This book is an invaluable resource for aspiring network administrators aiming to deepen their understanding of networking concepts while strengthening their C++ programming skills. Across eleven chapters, this book bridges the gap between network administration and programming, providing readers with a holistic approach to mastering network operations. Readers begin with a deep dive into network fundamentals such as TCP/IP models, sockets, and protocols. They then progress to practical programming, employing C++ to establish TCP/UDP client-server connections, handle network errors, and deal with application layer protocols such as HTTP/HTTPS, FTP, SMTP, IMAP, and DNS.The book then guides readers through Virtual Private Networks (VPNs), detailing their importance, functioning, and distinct types of VPNs. It explores wireless networking and asynchronous programming, providing clear illustrations of WiFi, Bluetooth, and Zigbee setup using C++. It covers critical wireless standards and security protocols. For a comprehensive understanding, the book illustrates network configuration management using C++ to automate crucial network operations tasks, thus highlighting the power of programming in network management.Advanced topics include network testing and simulations, which provide insights into performance enhancement and network robustness. A detailed exploration of network monitoring enhances the reader's skillset, teaching ways to conduct fault, performance, security, and account monitoring. In the end, the book rounds up with network troubleshooting, elucidating several essential network troubleshooting tools and methodologies.Key LearningsUnderstand TCP/IP model and protocols with hands-on C++ programming.Master TCP/UDP client-server connections and error handling.Grasp application layer protocols like HTTP/HTTPS, FTP, SMTP, IMAP, and DNS.Discover the importance and use of VPNs and how to set them up.Learn about wireless networking and asynchronous programming.Gain insights into network configuration management.Understand network testing methodologies and simulations.Learn to monitor various aspects of a network using Nagios.Learn about essential network troubleshooting tools and methodologies.Enhance network performance and reliability through C++ programming.The essence of this book lies in its practical approach. With ample illustrations, code snippets, and hands-on exercises using C++, this book stands out as a definitive guide for anyone aiming to become a competent network administrator, equipped with the power of programming.Table of ContentsIntroduction to Networking and C++Understanding Internet Protocols - TCP and UDPNetwork Interfaces and AddressingApplication Layer ProtocolsVPNsWireless NetworksAsynchronous ProgrammingNetwork Testing and SimulationNetwork Configuration and ManagementNetwork MonitoringNetwork TroubleshootingAudienceThis book is suitable for every computer programmer or computer science graduate with a basic understanding of C++. No prior networking knowledge is required. Familiarity with fundamental C++ concepts, such as variables, loops, and basic syntax, is assumed. By focusing on practical examples and clear explanations, this guide ensures a fast-paced learning experience.

Python Networking 101 is the ultimate guide for aspiring network administrators looking to build their network management and automation skills using Python. With a comprehensive and hands-on approach, this book covers the most important aspects of networking, including network fundamentals, network automation, monitoring, security, topology, and testing.The book begins with an overview of the Python language and its libraries used for networking tasks. Each chapter then focuses on a specific networking task, providing readers with a deep understanding of the topic and practical demonstrations using Python libraries. By the end of each chapter, readers will be well-versed in the execution and implementation of these tasks.Throughout the book, readers will learn about the best Python libraries network administrators prefer, including Netmiko, Paramiko, SNMP, Flask, AsyncIO, and more. Practical examples and exercises will help them gain hands-on experience working with these libraries to achieve various networking objectives. The book also discusses advanced network automation techniques, providing insights into network automation frameworks, such as Ansible, and how to build custom network automation solutions using Python. By the end of the book, readers will be equipped with the knowledge to integrate Python with network management tools, making them efficient and effective network administrators.Key LearningsMaster Python language and its networking libraries for network administration tasks.Monitor and analyze network performance and troubleshoot issues effectively.Enhance network security using Python libraries and best practices.Get well-versed with Netmiko, Paramiko, Socket, PySNMP, AsyncIO, and SimPy.Develop custom network services and interact with RESTful APIs using Python.Improve performance with asynchronous programming using AsyncIO in network applications.Get hands-on with Ansible to create playbooks and perform every possible network automation.Perform network testing and simulation, and analyze results for optimized performance.Manage and automate network configuration changes and ensure compliance.Leverage advanced network automation techniques and frameworks for efficient administration.Table of ContentIntroduction to Python and Networking LibrariesTCP, UDP and Socket ProgrammingWorking with Application LayerExploring Network AutomationNetwork Monitoring and AnalysisNetwork Security and PythonWorking with APIs and Network ServicesNetwork Programming with AsyncIONetwork Testing and SimulationNetwork Configuration ManagementAnsible and PythonAudience"Python Networking 101" is designed to provide readers with the skills required to excel as a network administrators. The practical approach, coupled with real-world examples, ensures readers can implement the techniques learned in their professional careers. Knowing Python and the basics of computer networks is sufficient, to begin with this book.

SQL 101 Crash Course is a comprehensive beginner's guide that takes you through the world of SQL, right from understanding databases to mastering complex queries. This book is designed to provide you with a solid foundation in SQL, along with practical examples and real-world scenarios to reinforce your learning. In this book, you'll explore the key concepts of databases and their structure while getting started with SQLite Studio, a versatile SQL tool. You'll dive deep into the fundamentals of SQL queries, turning raw data into meaningful information, and working with tables, multiple tables, and their relationships.You'll also learn how to harness the power of SQL functions and subqueries to optimize your queries and retrieve data more efficiently. As you progress, you'll delve into the world of views, joins, and advanced SQL topics such as transactions, stored procedures, and performance tuning. The book concludes with two sample databases, where you'll put your newfound knowledge to the test and gain hands-on experience.This book promises a smooth learning journey for aspiring SQL developers, enabling them to build robust and efficient databases. The book's step-by-step approach ensures that even complete beginners can grasp complex concepts with ease. By the end of this book, you'll emerge as a smart SQL developer, equipped with the skills and knowledge to tackle real-world database challenges.Key LearningsMaster SQL fundamentals and best practices.Learn to create, modify, and optimize tables.Understand and implement table relationships.Execute complex queries with ease and confidence.Leverage SQL functions for powerful data manipulation.Utilize subqueries and derived tables effectively.Create and manage views for enhanced data access.Apply advanced SQL techniques for optimized performance.Hands-on experience with real-world sample databases.Begin your journey as a skilled SQL developer.Table of ContentIntroduction to Databases and SQLSetting Up Your SQL EnvironmentSQL Queries BasicsTurning Data into InformationWorking with TablesMultiple Tables and JoinsSQL FunctionsSubqueries and Derived TablesViews and Materialized ViewsAdvanced SQL TopicsSample Programs & Executing SQLAudienceThis book requires no prior knowledge to get started, making it an ideal read for those looking to pursue careers in database administration, business analytics, or business intelligence. Its accessibility ensures that an unwavering passion for learning SQL is all you need to effortlessly progress through the book's content.

'Day Trading with ChatGPT' is an experimentation guide that explores how the powerful AI language model ChatGPT can be utilized for day trading signals in the stock market. This pioneering book aims to give readers a hands-on experience and a comprehensive understanding of how to experiment with ChatGPT for better decision-making before considering it a trading tool.The author takes a critical approach, emphasizing the strengths and limitations of using ChatGPT in trading. As you journey through the pages, you'll discover the AI's impressive abilities to analyze historical data, address financial prompts, and offer decision-making input while acknowledging the potential pitfalls of relying solely on AI-driven analysis.The book's objective is not to advocate for ChatGPT as the ultimate trading solution but to objectively examine its potential and limitations in the financial world. The author subtly highlights their skepticism, encouraging readers to approach the technology with a discerning eye and always to corroborate AI-generated insights with their research and expertise.Key LearningsDiscover how ChatGPT can analyze historical data for trading insights.Learn to leverage ChatGPT's ability to address financial prompts.Enhance decision-making with AI-driven input in day trading.Understand the importance of combining AI with human expertise.Explore the benefits and limitations of AI in financial analysis.Master the use of technical indicators with ChatGPT's guidance.Develop a critical approach to AI-generated trading insights.Improve your trading strategies by incorporating AI tools.Gain a comprehensive understanding of ChatGPT's capabilities.Learn to navigate the financial world with AI-assisted decision-making.Table of ContentPower of AI in Stock Market PredictionsCollecting and Analyzing Historical Stock DataMoving Averages (SMA and EMA) with ChatGPTRelative Strength Index (RSI) with ChatGPTBollinger Bands with ChatGPTFibonacci Retracement with ChatGPTMoving Average Convergence Divergence (MACD) with ChatGPTStochastic Oscillator with ChatGPTPutting It All Together - Is It Worth Using ChatGPT?