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Machine Learning Systems - Jeff Smith - Bog - Manning Publications - Plusbog.dk

Machine Learning Systems - Jeff Smith - Bog - Manning Publications - Plusbog.dk

Summary Machine Learning Systems: Designs that scale is an example-rich guide that teaches you how to implement reactive design solutions in your machine learning systems to make them as reliable as a well-built web app. Foreword by Sean Owen, Director of Data Science, Cloudera Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology If you’re building machine learning models to be used on a small scale, you don't need this book. But if you're a developer building a production-grade ML application that needs quick response times, reliability, and good user experience, this is the book for you. It collects principles and practices of machine learning systems that are dramatically easier to run and maintain, and that are reliably better for users. About the Book Machine Learning Systems: Designs that scale teaches you to design and implement production-ready ML systems. You'll learn the principles of reactive design as you build pipelines with Spark, create highly scalable services with Akka, and use powerful machine learning libraries like MLib on massive datasets. The examples use the Scala language, but the same ideas and tools work in Java, as well. What's Inside - - Working with Spark, MLlib, and Akka - - Reactive design patterns - - Monitoring and maintaining a large-scale system - - Futures, actors, and supervision - About the Reader Readers need intermediate skills in Java or Scala. No prior machine learning experience is assumed. About the Author Jeff Smith builds powerful machine learning systems. For the past decade, he has been working on building data science applications, teams, and companies as part of various teams in New York, San Francisco, and Hong Kong. He blogs (https: //medium.com/@jeffksmithjr), tweets (@jeffksmithjr), and speaks (www.jeffsmith.tech/speaking) about various aspects of building real-world machine learning systems. Table of Contents PART 1 - FUNDAMENTALS OF REACTIVE MACHINE LEARNING 1) 1) Learning reactive machine learning 1) 1) Using reactive tools 1) PART 2 - BUILDING A REACTIVE MACHINE LEARNING SYSTEM 1) 1) Collecting data 1) 1) Generating features 1) 1) Learning models 1) 1) Evaluating models 1) 1) Publishing models 1) 1) Responding 1) PART 3 - OPERATING A MACHINE LEARNING SYSTEM 1) 1) Delivering 1) 1) Evolving intelligence 1)

DKK 370.00
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Machine Learning with TensorFlow - Nishant Shukla - Bog - Manning Publications - Plusbog.dk

Machine Learning with TensorFlow - Nishant Shukla - Bog - Manning Publications - Plusbog.dk

DESCRIPTION Being able to make near-real-time decisions is becoming increasingly crucial. To succeed, we need machine learning systems that can turn massive amounts of data into valuable insights. But when you''re just starting out in the data science field, how do you get started creating machine learning applications? The answer is TensorFlow, a new open source machine learning library from Google. The TensorFlow library can take your high level designs and turn them into the low level mathematical operations required by machine learning algorithms. Machine Learning with TensorFlow teaches readers about machine learning algorithms and how to implement solutions with TensorFlow. It starts with an overview of machine learning concepts and moves on to the essentials needed to begin using TensorFlow. Each chapter zooms into a prominent example of machine learning. Readers can cover them all to master the basics or skip around to cater to their needs. By the end of this book, readers will be able to solve classification, clustering, regression, and prediction problems in the real world. KEY FEATURES • Lots of diagrams, code examples, and exercises • Solves real-world problems with TensorFlow • Uses well-studied neural network architectures • Presents code that can be used for the readers’ own applications AUDIENCE This book is for programmers who have some experience with Python and linear algebra concepts like vectors and matrices. No experience with machine learning is necessary. ABOUT THE TECHNOLOGY Google open-sourced their machine learning framework called TensorFlow in late 2015 under the Apache 2.0 license. Before that, it was used proprietarily by Google in its speech recognition, Search, Photos, and Gmail, among other applications. TensorFlow is one the most popular machine learning libraries.

DKK 349.00
1

Real-World Machine Learning - Henrick Brink - Bog - Manning Publications - Plusbog.dk

Real-World Machine Learning - Henrick Brink - Bog - Manning Publications - Plusbog.dk

DESCRIPTION In a world where big data is the norm and near-real-time decisions are crucial, machine learning (ML) is a critical component of the data workflow. Machine learning systems can quickly crunch massive amounts of information to offer insights and make decisions in a way that matches or even surpasses human cognitive abilities. These systems use sophisticated computational and statistical tools to build models that can recognize and visualize patterns, predict outcomes, forecast values, and make recommendations. Real-World Machine Learning is a practical guide designed to teach developers the art of ML project execution. The book introduces the day-to-day practice of machine learning and prepares readers to successfully build and deploy powerful ML systems. Using the Python language and the R statistical package, it starts with core concepts like data acquisition and modeling, classification, and regression. Then it moves through the most important ML tasks, like model validation, optimization and feature engineering. It uses real-world examples that help readers anticipate and overcome common pitfalls. Along the way, they will discover scalable and online algorithms for large and streaming data sets. Advanced readers will appreciate the in-depth discussion of enhanced ML systems through advanced data exploration and pre-processing methods. KEY FEATURES - - Accessible and practical introduction to machine learning - - Contains big-picture ideas and real-world examples - - Prepares reader to build and deploy powerful predictive systems - - Offers tips & tricks and highlights common pitfalls - AUDIENCE Code examples are in Python and R. No prior machine learning experience required. ABOUT THE TECHNOLOGY Machine learning has gained prominence due to the overwhelming successes of Google, Microsoft, Amazon, LinkedIn, Facebook, and others in their use of ML. The Gartner report predicts that big data analytics will be a $25 billion market by 2017, and financial firms, marketing organizations, scientific facilities, and Silicon Valley startups are all demanding machine learning skills from their developers.

DKK 380.00
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Natural Language Processing in Action - Hapke Hannes - Bog - Manning Publications - Plusbog.dk

Natural Language Processing in Action - Hapke Hannes - Bog - Manning Publications - Plusbog.dk

Description Modern NLP techniques based on machine learning radically improve the ability of software to recognize patterns, use context to infer meaning, and accurately discern intent from poorly-structured text. In Natural Language Processing in Action , readers explore carefully chosen examples and expand their machine''s knowledge which they can then apply to a range of challenges. Key Features • Easy-to-follow • Clear examples • Hands-on-guide Audience A basic understanding of machine learning and some experience with a modern programming language such as Python, Java, C++, or JavaScript will be helpful. About the technology Natural Language Processing (NLP) is the discipline of teaching computers to read more like people, and readers can see examples of it in everything from chatbots to the speech-recognition software on their phone. Hobson Lane has more than 15 years of experience building autonomous systems that make important decisions on behalf of humans. Hannes Hapke is an Electrical Engineer turned Data Scientist with experience in deep learning. Cole Howard is a carpenter and writer turned Deep Learning expert.

DKK 382.00
1

Deep Reinforcement Learning in Action - Brandon Brown - Bog - Manning Publications - Plusbog.dk

Deep Reinforcement Learning in Action - Brandon Brown - Bog - Manning Publications - Plusbog.dk

Humans learn best from feedback—we are encouraged to take actions that lead to positive results while deterred by decisions with negative consequences. This reinforcement process can be applied to computer programs allowing them to solve more complex problems that classical programming cannot. Deep Reinforcement Learning in Action teaches you the fundamental concepts and terminology of deep reinforcement learning, along with the practical skills and techniques you’ll need to implement it into your own projects. Key features • Structuring problems as Markov Decision Processes • Popular algorithms such Deep Q-Networks, Policy Gradient method and Evolutionary Algorithms and the intuitions that drive them • Applying reinforcement learning algorithms to real-world problems Audience You’ll need intermediate Python skills and a basic understanding of deep learning. About the technology Deep reinforcement learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep reinforcement learning famously contributed to the success of AlphaGo but that’s not all it can do! Alexander Zai is a Machine Learning Engineer at Amazon AI working on MXNet that powers a suite of AWS machine learning products. Brandon Brown is a Machine Learning and Data Analysis blogger at outlace.com committed to providing clear teaching on difficult topics for newcomers.

DKK 382.00
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Algorithms of the Intelligent Web, Second Edition - Dmitry Babenko - Bog - Manning Publications - Plusbog.dk

Algorithms of the Intelligent Web, Second Edition - Dmitry Babenko - Bog - Manning Publications - Plusbog.dk

DESCRIPTION There''s priceless insight trapped in the flood of data users leave behind as they interact with web pages and applications. Those insights can be unlocked by using intelligent algorithms like the ones that have earned Facebook, Google, Twitter, and Microsoft a place among the giants of web data pattern extraction. Improved search, data classification, and other smart pattern matching techniques can give an enormous advantage to understanding and interacting with users. Algorithms of the Intelligent Web, Second Edition has been totally revised and teaches the most important approaches to algorithmic web data analysis, enabling readers to create machine learning applications that crunch, munge, and wrangle data collected from users, web applications, sensors, and website logs. Key machine learning concepts are explained and introduced with many code examples in Python''s scikit-learn. The book guides readers through the underlying machinery and intelligent algorithms to capture, store, and structure data streams. Readers will explore recommendation engines from the example of Netflix movie recommendations and dive into classification via statistical algorithms, neural networks, and deep learning. They will also consider the ins and outs of ranking and how to test applications based on intelligent algorithms. KEY SELLING POINTS Machine learning for newbies Easily accessed examples Concepts presented are technology agnostic AUDIENCE To get the most from this book, you should have a good foundation in Java programming and a general understanding of internet technology. ABOUT THE TECHNOLOGY This book provides an overview, with easy to access examples, of algorithms which learn from data. Such algorithms have been widely adopted by many large internet companies such as Facebook and Google and are continuing to grow in popularity. This book has many examples in Python using the scikit-learn library, however the concepts presented are technology agnostic and can be easily applied with any common programming language.

DKK 364.00
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TypeScript Quickly - Yakov Fain - Bog - Manning Publications - Plusbog.dk

TypeScript Quickly - Yakov Fain - Bog - Manning Publications - Plusbog.dk

Thanks to the authors’ easy-to-digest style, you’ll effortlessly learn about types, object-oriented programming with classes and interfaces, and using TypeScript with JavaScript libraries. You’ll discover TypeScript’s excellent tooling as you explore code-quality improvement with TSLint, debugging with source maps, unit testing, and more. TypeScript is JavaScript with an important upgrade! By adding a strong type system to JavaScript, TypeScript can help you eliminate entire categories of runtime errors. In TypeScript Quickly , you’ll learn to build rock-solid apps through practical examples and hands-on projects under the expert instruction of experienced web developers Yakov Fain and Anton Moiseev. WILL SELL LIKE Angular Development with Typescript, Key features • Mastering TypeScript syntax • Object-oriented programming with classes and interfaces • Using TypeScript with JavaScript libraries • Multiple real-world code samples Audience Written for intermediate web developers comfortable with JavaScript ES5 and HTML. About the technology TypeScript is an extension of JavaScript that includes key language features such as optional static typing, compile-time error catching, and auto-complete. By specifying types and type annotations, your code becomes much easier to interpret, which improves productivity and team development. In particular, TypeScript makes complex applications like SPAs much easier to maintain and extend. Yakov Fain and Anton Moiseev are experienced web application developers. They authored two editions of Manning’s Angular Development with TypeScript among other technical books. Yakov is a Java champion and a prolific tech blogger at yakov.fain.com. Yakov Fain and Anton Moiseev are experienced web application developers. They authored two editions of Manning’s Angular Development with TypeScript among other technical books. Yakov is a Java champion and a prolific tech blogger at yakov.fain.com.

DKK 382.00
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Classic Computer Science Problems in Python - David Kopec - Bog - Manning Publications - Plusbog.dk

Classic Computer Science Problems in Python - David Kopec - Bog - Manning Publications - Plusbog.dk

Classic Computer Science Problems in Python presents dozens of coding challenges, ranging from simple tasks like finding items in a list with a binary sort algorithm to clustering data using k-means. Classic Computer Science Problems in Python deepens your Python language skills by challenging you with time-tested scenarios, exercises, and algorithms. As you work through examples in search, clustering, graphs, and more, you''ll remember important things you''ve forgotten and discover classic solutions to your "new" problems Key Features · Breadth-first and depth-first search algorithms · Constraints satisfaction problems · Common techniques for graphs · Adversarial Search · Neural networks and genetic algorithms · Written for data engineers and scientists with experience using Python. For readers comfortable with the basics of Python About the technology Python is used everywhere for web applications, data munging, and powerful machine learning applications. Even problems that seem new or unique stand on the shoulders of classic algorithms, coding techniques, and engineering principles. Master these core skills, and you’ll be ready to use Python for AI, data-centric programming, deep learning, and the other challenges you’ll face as you grow your skill as a programmer. David Kopec teaches at Champlain College in Burlington, VT and is the author of Manning’s Classic Computer Science Problemsin Swift.

DKK 300.00
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Well-Grounded Python Developer, The - Doug Farrell - Bog - Manning Publications - Plusbog.dk

Well-Grounded Python Developer, The - Doug Farrell - Bog - Manning Publications - Plusbog.dk

When you''re new to Python, it can be tough to understand where and how to use its many language features. There''s a dizzying array of libraries, and it''s challenging to fit everything together. The Well- Grounded Python Developer builds on Python skills you''ve learned in isolation and shows you how to unify them into a meaningful whole. The Well-Grounded Python Developer teaches you how to write real software in Python by building on the basic language skills you already have. It helps you see the big picture you can create out of small pieces, introducing concepts like modular construction, APIs, and the design of a basic web server. When you''re finished, you''ll have gone from having a basic understanding of Python''s syntax, grammar, and libraries to using them as the tools of a professional software developer. About the Technology Python is the perfect language for beginning programmers. It is easy to learn, with tons of helpful libraries and tools. Better still, it doesn''t run out of steam when you want to create more advanced applications for web development or machine learning. Once you''ve mastered the syntax of simple Python scripts, it can be a challenge to progress to more ambitious projects. This book helps you on that path.

DKK 364.00
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Inside AI - Akli Adjaoute - Bog - Manning Publications - Plusbog.dk

Inside AI - Akli Adjaoute - Bog - Manning Publications - Plusbog.dk

Separate the AI facts from the AI fiction, and discover how you can best put these tools to work in your organization. In Inside AI AI professor and entrepreneur Dr. Akli Adjaoute puts AI in perspective, with informed insights from 30 years spent in the field. His book lays out a pragmatic blueprint that every leader can utilize to drive innovation with artificial intelligence. In Inside AI you’ll learn how to: - - Gain insight into diverse AI techniques and methodologies - - Learn from both successful and failed AI applications - - Identify the capabilities and limitations of AI systems - - Understand successful and failed uses of AI in business - - See where human cognition still exceeds AI - - Bust common myths like AI’s threat to jobs and civilization - - Manage AI projects effectively - Inside AI takes you on a journey through artificial intelligence, from AI’s origins in traditional expert systems all the way to deep learning and Large Language Models. There’s no hype here—you’ll get the grounded, evidence-based insights that are vital for making strategic decisions and preparing your business for the future. Foreword by Raymond Kendall. Purchase of the print book includes a free eBook in PDF and ePub formats from Manning Publications. About the technology Artificial Intelligence enthusiasts promise everything from human-like collaboration on everyday tasks to the end of work as we know it. Is AI just a flash in the pan, or can it really transform how you do business? This intriguing book sifts through the hype and separates the truth from the myths, with clear advice on what AI can—and can’t—achieve. About the book Inside AI provides a clear-headed overview of modern artificial intelligence, including the recent advances of Generative AI and Large Language Models. Its accessible and jargon-free explanations of leading AI techniques showcase how AI delivers tangible advantages to businesses. Both inspiring and practical, this book provides a proven framework for developing successful AI applications. What''s inside - - Insights from successful and failed AI applications - - A survey of AI techniques and methodologies - - Bust common AI myths - - Manage AI projects effectively - About the reader For anyone seeking grounded insights into AI’s capabilities, including business leaders and decision makers. About the author Akli Adjaoute is the founder of multiple AI-related companies. He served as an adjunct professor at the University of San Francisco and as Scientific Committee Chair and Head of the AI department at EPITA. The technical editor on this book was Richard Vaughan . Table of contents 1 The rise of machine intelligence 2 AI mastery: Essential techniques, Part 1 3 AI mastery: Essential techniques, Part 2 4 Smart agent technology 5 Generative AI and large language models 6 Human vs. machine 7 AI doesn’t turn data into intelligence 8 AI doesn’t threaten our jobs 9 Technological singularity is absurd 10 Learning from successful and failed applications of AI 11 Next-generation AI A Tracing the roots: From mechanical calculators to digital dreams B Algorithms and programming languages

DKK 250.00
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Getting Started with Natural Language Processing - Ekaterina Kochmar - Bog - Manning Publications - Plusbog.dk

Getting Started with Natural Language Processing - Ekaterina Kochmar - Bog - Manning Publications - Plusbog.dk

Getting Started with Natural Language Processing is a hands-on guide filled with everything you need to get started with NLP in a friendly, understandable tutorial. Full of Python code and hands-on projects, each chapter provides a concrete example with practical techniques that you can put into practice right away. By following the numerous Python-based examples and real-world case studies, you’ll apply NLP to search applications, extracting meaning from text, sentiment analysis, user profiling, and more. When you’re done, you’ll have a solid grounding in NLP that will serve as a foundation for further learning. Key Features · Extracting information from raw text · Named entity recognition · Automating summarization of key facts · Topic labeling For beginners to NLP with basic Python skills. About the technology Natural Language Processing is a set of data science techniques that enable machines to make sense of human text and speech. Advances in machine learning and deep learning have made NLP more efficient and reliable than ever, leading to a huge number of new tools and resources. From improving search applications to sentiment analysis, the possible applications of NLP are vast and growing. Ekaterina Kochmar is an Affiliated Lecturer and a Senior Research Associate at the Natural Language and Information Processing group of the Department of Computer Science and Technology, University of Cambridge. She holds an MA degree in Computational Linguistics, an MPhil in Advanced Computer Science, and a PhD in Natural Language Processing.

DKK 335.00
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Demand Forecasting Best Practices - Nicolas Vandeput - Bog - Manning Publications - Plusbog.dk

Demand Forecasting Best Practices - Nicolas Vandeput - Bog - Manning Publications - Plusbog.dk

Master the demand forecasting skills you need to decide what resources to acquire, products to produce, and where and how to distribute them. For demand planners, S&OP managers, supply chain leaders, and data scientists. Demand Forecasting Best Practices is a unique step-by-step guide, demonstrating forecasting tools, metrics, and models alongside stakeholder management techniques that work in a live business environment. You will learn how to: - - Lead a demand planning team to improve forecasting quality while reducing workload - - Properly define the objectives, granularity, and horizon of your demand planning process - - Use smart, value-weighted KPIs to track accuracy and bias - - Spot areas of your process where there is room for improvement - - Help planners and stakeholders (sales, marketing, finances) add value to your process - - Identify what kind of data you should be collecting, and how - - Utilise different types of statistical and machine learning models - Follow author Nicolas Vandeput''s original five-step framework for demand planning excellence and learn how to tailor it to your own company''s needs. You will learn how to optimise demand planning for a more effective supply chain and will soon be delivering accurate predictions that drive major business value. About the technology Demand forecasting is vital for the success of any product supply chain. It allows companies to make better decisions about what resources to acquire, what products to produce, and where and how to distribute them. As an effective demand forecaster, you can help your organisation avoid overproduction, reduce waste, and optimise inventory levels for a real competitive advantage.

DKK 326.00
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Data Pipelines with Apache Airflow - Bas Harenslak - Bog - Manning Publications - Plusbog.dk

Data Pipelines with Apache Airflow - Bas Harenslak - Bog - Manning Publications - Plusbog.dk

Pipelines can be challenging to manage, especially when your data has to flow through a collection of application components, servers, and cloud services. Airflow lets you schedule, restart, and backfill pipelines, and its easy-to-use UI and workflows with Python scripting has users praising its incredible flexibility. Data Pipelines with Apache Airflow takes you through best practices for creating pipelines for multiple tasks, including data lakes, cloud deployments, and data science. Data Pipelines with Apache Airflow teaches you the ins-and-outs of the Directed Acyclic Graphs (DAGs) that power Airflow, and how to write your own DAGs to meet the needs of your projects. With complete coverage of both foundational and lesser-known features, when you’re done you’ll be set to start using Airflow for seamless data pipeline development and management. Key Features Framework foundation and best practices Airflow''s execution and dependency system Testing Airflow DAGs Running Airflow in production For data-savvy developers, DevOps and data engineers, and system administrators with intermediate Python skills. About the technology Data pipelines are used to extract, transform and load data to and from multiple sources, routing it wherever it’s needed -- whether that’s visualisation tools, business intelligence dashboards, or machine learning models. Airflow streamlines the whole process, giving you one tool for programmatically developing and monitoring batch data pipelines, and integrating all the pieces you use in your data stack. Bas Harenslak and Julian de Ruiter are data engineers with extensive experience using Airflow to develop pipelines for major companies including Heineken, Unilever, and Booking.com. Bas is a committer, and both Bas and Julian are active contributors to Apache Airflow.

DKK 382.00
1

Relevant Search - Doug Turnbull - Bog - Manning Publications - Plusbog.dk

Relevant Search - Doug Turnbull - Bog - Manning Publications - Plusbog.dk

DESCRIPTION Users expect search to be simple: They enter a few terms and expect perfectly-organized, relevant results instantly. But behind this simple user experience, complex machinery is at work. Whether using Elasticsearch, Solr, or another search technology, the solution is never one size fits all. Returning the right search results requires conveying domain knowledge and business rules in the search engine''s data structures, text analytics, and results ranking capabilities. Relevant Search demystifies relevance work. Using Elasticsearch, it tells how to return engaging search results to users, helping readers understand and leverage the internals of Lucene-based search engines. The book walks through several real-world problems using a cohesive philosophy that combines text analysis, query building, and score shaping to express business ranking rules to the search engine. It outlines how to guide the engineering process by monitoring search user behavior and shifting the enterprise to a search-first culture focused on humans, not computers. It also shows how the search engine provides a deeply pluggable platform for integrating search ranking with machine learning, ontologies, personalization, domain-specific expertise, and other enriching sources. KEY FEATURES Highly relevant, concrete, hands-on guide Digs deep into search engine technology Contains essential tools, tips, and strategies for building engaging search engines AUDIENCE For readers who can code moderately complex tasks. ABOUT THE TECHNOLOGY Lucene is the underlying technology that backs both Elasticsearch and Solr. Dominant search engines are based upon Lucene and since Lucene itself is based upon the strong foundation of Information Retrieval research, the book will be applicable to almost any search technology available now or in the foreseeable future.

DKK 364.00
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Regular Expression Puzzles and AI Coding Assistants: 24 puzzles solved by the author, with and without assistance from Copilot, ChatGPT and more -

Regular Expression Puzzles and AI Coding Assistants: 24 puzzles solved by the author, with and without assistance from Copilot, ChatGPT and more -

Learn how AI-assisted coding using ChatGPT and GitHub Copilot can dramatically increase your productivity (and fun) in writing regular expressions and other programmes. "How these tools can be both so very amazing in what they produce, and simultaneously so utterly doltish in their numerous failures, is the main thing this book tries to understand. For reasons I attempt to elucidate throughout, of all the domains of computer programming, games with regular expressions are particularly well suited for getting a grasp on the peculiar behaviors of AI." From the Preface For programmers of any experience level – no experience with AI coding tools is required. Regular Expression Puzzles and AI Coding Assistants is the story of two competitors. On the one side is David Mertz, an expert programmer and the author of the Web''s most popular Regex tutorial. On the other are the AI powerhouse coding assistants, GitHub Copilot and OpenAI ChatGPT. Here''s how the contest works: David invents 24 Regex problems he calls puzzles and shows you how to tackle each one. When he''s done he has Copilot and ChatGPT work the same puzzles. What they produce intrigues him. Which side is likelier to get it right? Which will write simple and elegant code? Which one makes the smartest use of lesser-known Regex library features? Read the book to find out. David also offers AI best practices, showing how smart prompts return better results. By the end, you''ll be a master at solving your own Regex puzzles, whether you use AI or not. About the technology Ground-breaking large language model research from OpenAI, Google, Amazon, and others, have transformed expectations of machine-generated software. But how do these AI assistants, like ChatGPT and GitHub Copilot, measure up against regular expressions—a workhorse technology for developers used to describe, find, and manipulate patterns in the text? Regular expressions are compact, complex, and subtle. Will AI assistants handle the challenge?

DKK 312.00
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Learn PowerShell in a Month of Lunches: Covers Windows, Linux, and macOS - Travis Plunk - Bog - Manning Publications - Plusbog.dk

Learn PowerShell in a Month of Lunches: Covers Windows, Linux, and macOS - Travis Plunk - Bog - Manning Publications - Plusbog.dk

"Not only for MacOS and Linux users, but also a great resource for Windows PS users." - Bruce Bergman Learn PowerShell in a Month of Lunches: Covers Windows, Linux, and macOS is a task-focused tutorial for administering Linux and macOS systems using Microsoft PowerShell. Adapted by PowerShell team members Travis Plunk and Tyler Leonhardt from the bestselling Learn Windows PowerShell in a Month of Lunches by community legends Don Jones and Jeffrey Hicks, it features Linux-based examples covering core language features and admin tasks. Designed for busy IT professionals, this innovative guide will take you from the basics to PowerShell proficiency through 25 tutorials you can do in your lunch break. about the technology The PowerShell scripting language and administrative shell was initially created for Windows, providing a high-quality command-line interface and awesome automation features. As part of Microsoft''s ongoing strategy to support non-Windows platforms with its Azure cloud service and .NET Core framework, PowerShell now runs on Linux and macOS. Like Bash, PowerShell can execute and script nearly any aspect of Linux, so you can easily manage repetitive daily tasks, servers, Cloud resources, Continuous Integration pipelines, and more. Because PowerShell is a full-featured programming language, however, it provides capability well beyond traditional shell scripting languages, such as the ability to treat OS components as objects. about the book Learn PowerShell in a Month of Lunches: Covers Windows, Linux, and macOS is a user-friendly tutorial to managing Linux and macOS systems with PowerShell. It''s based on the bestselling Learn Windows PowerShell in a Month of Lunches, which has introduced PowerShell to nearly 100,000 readers. You''ll learn how PowerShell shapes up to Bash or Python scripting as you write and run simple scripts that automate boring daily tasks. As you progress through the book, you''ll use PowerShell to write Continuous Integration Pipelines and manage cloud-based servers. Just set aside one hour a day for a month, and you''ll be automating tasks faster than you ever thought possible! what''s inside - Why you should use PowerShell on Linux and macOS- Background jobs and automation techniques- Simple scripting to automate repetitive daily tasks- Common syntax and commands cheat sheet- Each lesson takes you an hour or less about the reader For IT professionals comfortable administering Windows or Linux. No previous experience with PowerShell or Bash required. about the author Travis Plunk has been a Software Engineer on various PowerShell teams since 2013, and at Microsoft since 1999. He was involved in open sourcing PowerShell and has worked on the project full time since shortly after the project was announced. James Petty is a Microsoft MVP, and the CEO and Executive Director for the DevOps Collective and PowerShell.org. Tyler Leonhardt has been a Software Engineer on the PowerShell team since 2017, and at Microsoft since 2016. He is a core maintainer of the PowerShell extension for Visual Studio Code. Learn Windows PowerShell in a Month of Lunches was written by PowerShell community legends Don Jones and Jeffrey Hicks, who have years of experience as successful PowerShell trainers.

DKK 335.00
1