Certified Kubernetes Administrator (CKA) Study Guide
The ability to administer and monitor a Kubernetes cluster is in high demand today. To meet this need, the Cloud Native Computing Foundation developed a certification exam to establish an administrator's credibility and value in the job market to confidently work in a Kubernetes environment. The Certified Kubernetes Administrator (CKA) certification exam is different from the typical multiple-choice format of other professional certifications. Instead, the CKA is a performance-based exam that requires deep knowledge of the tasks under immense time pressure. This study guide walks you through all the topics covered to fully prepare you for the exam. Author Benjamin Muschko also shares his personal experience with preparing for all aspects of the exam. Learn when and how to apply Kubernetes concepts to administer and troubleshoot a production-grade cluster; Understand the objectives, abilities, and tips and tricks needed to pass the CKA exam; Explore the ins and outs of the kube ...
The ability to administer and monitor a Kubernetes cluster is in high demand today. To meet this need, the Cloud Native Computing Foundation developed a certification exam to establish an administrator's credibility and value in the job market to confidently work in a Kubernetes environment. The Certified Kubernetes Administrator (CKA) certification exam is different from the typical multiple-choice format of other professional certifications. Instead, the CKA is a performance-based exam that requires deep knowledge of the tasks under immense time pressure. This study guide walks you through all the topics covered to fully prepare you for the exam. Author Benjamin Muschko also shares his personal experience with preparing for all aspects of the exam. Learn when and how to apply Kubernetes concepts to administer and troubleshoot a production-grade cluster; Understand the objectives, abilities, and tips and tricks needed to pass the CKA exam; Explore the ins and outs of the kube ...
Designing Autonomous AI
Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world. Using concrete examples, minimal theory, and a proven architectural framework, author Kence Anderson demonstrates how to teach autonomous AI explicit skills and strategies. You'll learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. Students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes can use the methodolo ...
Early rules-based artificial intelligence demonstrated intriguing decision-making capabilities but lacked perception and didn't learn. AI today, primed with machine learning perception and deep reinforcement learning capabilities, can perform superhuman decision-making for specific tasks. This book shows you how to combine the practicality of early AI with deep learning capabilities and industrial control technologies to make robust decisions in the real world. Using concrete examples, minimal theory, and a proven architectural framework, author Kence Anderson demonstrates how to teach autonomous AI explicit skills and strategies. You'll learn when and how to use and combine various AI architecture design patterns, as well as how to design advanced AI without needing to manipulate neural networks or machine learning algorithms. Students, process operators, data scientists, machine learning algorithm experts, and engineers who own and manage industrial processes can use the methodolo ...
Introduction to Python for Computational Science and Engineering
This book summarises a number of core ideas relevant to Computational Engineering and Scientific Computing using Python. The emphasis is on introducing some basic Python (programming) concepts that are relevant for numerical algorithms. The later chapters touch upon numerical libraries such as numpy and scipy each of which deserves much more space than provided here. We aim to enable the reader to learn independently how to use other functionality of these libraries using the available documentation (online and through the packages itself). ...
This book summarises a number of core ideas relevant to Computational Engineering and Scientific Computing using Python. The emphasis is on introducing some basic Python (programming) concepts that are relevant for numerical algorithms. The later chapters touch upon numerical libraries such as numpy and scipy each of which deserves much more space than provided here. We aim to enable the reader to learn independently how to use other functionality of these libraries using the available documentation (online and through the packages itself). ...
Inside Deep Learning
Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped - you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, ...
Inside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skipped - you'll dive into math, theory, and practical applications. Everything is clearly explained in plain English. Deep learning doesn't have to be a black box! Knowing how your models and algorithms actually work gives you greater control over your results. And you don't have to be a mathematics expert or a senior data scientist to grasp what's going on inside a deep learning system. This book gives you the practical insight you need to understand and explain your work with confidence. Inside Deep Learning illuminates the inner workings of deep learning algorithms in a way that even machine learning novices can understand. You'll explore deep learning concepts and tools through plain language explanations, ...
Functional Programming in Kotlin
Functional Programming in Kotlin is a reworked version of the bestselling Functional Programming in Scala, with all code samples, instructions, and exercises translated into the powerful Kotlin language. In this authoritative guide, you'll take on the challenge of learning functional programming from first principles. Complex concepts are demonstrated through exercises that you'll love to test yourself against. You'll start writing Kotlin code that's easier to read, easier to reuse, better for concurrency, and less prone to bugs and errors. Improve performance, increase maintainability, and eliminate bugs! How? By programming the functional way. Kotlin provides strong support for functional programming, taking a pragmatic approach that integrates well with OO codebases. By applying the techniques you'll learn in this book, your code will be safer, less prone to errors, and much easier to read and reuse. Functional Programming in Kotlin teaches you how to design and write Kotlin a ...
Functional Programming in Kotlin is a reworked version of the bestselling Functional Programming in Scala, with all code samples, instructions, and exercises translated into the powerful Kotlin language. In this authoritative guide, you'll take on the challenge of learning functional programming from first principles. Complex concepts are demonstrated through exercises that you'll love to test yourself against. You'll start writing Kotlin code that's easier to read, easier to reuse, better for concurrency, and less prone to bugs and errors. Improve performance, increase maintainability, and eliminate bugs! How? By programming the functional way. Kotlin provides strong support for functional programming, taking a pragmatic approach that integrates well with OO codebases. By applying the techniques you'll learn in this book, your code will be safer, less prone to errors, and much easier to read and reuse. Functional Programming in Kotlin teaches you how to design and write Kotlin a ...
Functional Programming in C#, 2nd Edition
Functional Programming in C# has helped thousands of developers apply functional thinking to C# code. Its practical examples and spot-on treatment of FP concepts makes it the perfect guide for proficient C# programmers. This second edition is fully revised to cover new functional-inspired features in the most recent releases of C#, including tuples, async streams, pattern matching, and records. Each chapter is packed with awesome perspectives and epiphany moments on how functional programming can change the way you code. Turbocharge your C# code. Good functional techniques will improve concurrency, state management, event handling, and maintainability of your software. This book gives you practical answers to why, how, and where to add functional programing into your C# coding practice. Functional Programming in C#, Second Edition teaches functional thinking for real-world problems. It reviews the C# language features that allow you to program functionally and through many practi ...
Functional Programming in C# has helped thousands of developers apply functional thinking to C# code. Its practical examples and spot-on treatment of FP concepts makes it the perfect guide for proficient C# programmers. This second edition is fully revised to cover new functional-inspired features in the most recent releases of C#, including tuples, async streams, pattern matching, and records. Each chapter is packed with awesome perspectives and epiphany moments on how functional programming can change the way you code. Turbocharge your C# code. Good functional techniques will improve concurrency, state management, event handling, and maintainability of your software. This book gives you practical answers to why, how, and where to add functional programing into your C# coding practice. Functional Programming in C#, Second Edition teaches functional thinking for real-world problems. It reviews the C# language features that allow you to program functionally and through many practi ...
Grokking Machine Learning
Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. Grokking Machine Learning presents machine learning algorithm ...
Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. Grokking Machine Learning presents machine learning algorithm ...
Kubernetes Native Microservices with Quarkus and MicroProfile
Popular Java frameworks like Spring were designed long before Kubernetes and the microservices revolution. Kubernetes Native Microservices with Quarkus and MicroProfile introduces next generation tools that have been cloud-native and Kubernetes-aware right from the beginning. Written by veteran Java developers John Clingan and Ken Finnigan, this book shares expert insight into Quarkus and MicroProfile directly from contributors at Red Hat. You'll learn how to utilize these modern tools to create efficient enterprise Java applications that are easy to deploy, maintain, and expand. Build microservices efficiently with modern Kubernetes-first tools! Quarkus works naturally with containers and Kubernetes, radically simplifying the development and deployment of microservices. This powerful framework minimizes startup time and memory use, accelerating performance and reducing hosting cost. And because it's Java from the ground up, it integrates seamlessly with your existing JVM codebase. ...
Popular Java frameworks like Spring were designed long before Kubernetes and the microservices revolution. Kubernetes Native Microservices with Quarkus and MicroProfile introduces next generation tools that have been cloud-native and Kubernetes-aware right from the beginning. Written by veteran Java developers John Clingan and Ken Finnigan, this book shares expert insight into Quarkus and MicroProfile directly from contributors at Red Hat. You'll learn how to utilize these modern tools to create efficient enterprise Java applications that are easy to deploy, maintain, and expand. Build microservices efficiently with modern Kubernetes-first tools! Quarkus works naturally with containers and Kubernetes, radically simplifying the development and deployment of microservices. This powerful framework minimizes startup time and memory use, accelerating performance and reducing hosting cost. And because it's Java from the ground up, it integrates seamlessly with your existing JVM codebase. ...
Understanding Machine Learning
The subject of this book is automated learning, or, as we will more often call it, Machine Learning (ML). That is, we wish to program computers so that they can "learn" from input available to them. Roughly speaking, learning is the process of converting experience into expertise or knowledge. The input to a learning algorithm is training data, representing experience, and the output is some expertise, which usually takes the form of another computer program that can perform some task. Seeking a formal-mathematical understanding of this concept, we'll have to be more explicit about what we mean by each of the involved terms: What is the training data our programs will access? How can the process of learning be automated? How can we evaluate the success of such a process (namely, the quality of the output of a learning program)? ...
The subject of this book is automated learning, or, as we will more often call it, Machine Learning (ML). That is, we wish to program computers so that they can "learn" from input available to them. Roughly speaking, learning is the process of converting experience into expertise or knowledge. The input to a learning algorithm is training data, representing experience, and the output is some expertise, which usually takes the form of another computer program that can perform some task. Seeking a formal-mathematical understanding of this concept, we'll have to be more explicit about what we mean by each of the involved terms: What is the training data our programs will access? How can the process of learning be automated? How can we evaluate the success of such a process (namely, the quality of the output of a learning program)? ...
Python Challenges
Augment your knowledge of Python with this entertaining learning guide, which features 100 exercises and programming puzzles and solutions. Python Challenges will help prepare you for your next exam or a job interview, and covers numerous practical topics such as strings, data structures, recursion, arrays, and more. Each topic is addressed in its own separate chapter, starting with an introduction to the basics and followed by 10 to 15 exercises of various degrees of difficulty, helping you to improve your programming skills effectively. Detailed sample solutions, including the algorithms used for all tasks, are included to maximize your understanding of each area. Author Michael Inden also describes alternative solutions and analyzes possible pitfalls and typical errors. Three appendices round out the book: the first covers the Python command line interpreter, which is often helpful for trying out the code snippets and examples in the book, followed by an overview of Pytest for ...
Augment your knowledge of Python with this entertaining learning guide, which features 100 exercises and programming puzzles and solutions. Python Challenges will help prepare you for your next exam or a job interview, and covers numerous practical topics such as strings, data structures, recursion, arrays, and more. Each topic is addressed in its own separate chapter, starting with an introduction to the basics and followed by 10 to 15 exercises of various degrees of difficulty, helping you to improve your programming skills effectively. Detailed sample solutions, including the algorithms used for all tasks, are included to maximize your understanding of each area. Author Michael Inden also describes alternative solutions and analyzes possible pitfalls and typical errors. Three appendices round out the book: the first covers the Python command line interpreter, which is often helpful for trying out the code snippets and examples in the book, followed by an overview of Pytest for ...