gitforgits jan 2024 (8 results)

Taschenbuch. Condition: Neu. Neuware -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 AILibri GmbH, Europaallee 1, 36244 Bad Hersfeld 186 pp. Englisch.

Taschenbuch. Condition: Neu. Neuware Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 262 pp. Englisch.

Taschenbuch. Condition: Neu. Neuware -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 RustLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 158 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 186 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 172 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 262 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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 158 pp. Englisch.

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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 RustLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 172 pp. Englisch.