Bøger udgivet af O'Reilly Media

If youre a web designer or app developer interested in sophisticated page styling, improved accessibility, and saving time and effort, this book is for you. This revised edition provides a comprehensive guide to CSS implementation, along with a thorough review of the latest CSS specifications.CSS is a constantly evolving language for describing the presentation of web content on screen, printers, speech synthesizers, screen readers, and chat windows. It is used by all browsers on all screen sizes on all types of IoT devices, including phones, computers, video games, televisions, watches, kiosks, and auto consoles. Authors Eric Meyer and Estelle Weyl show you how to improve user experience, speed development, avoid potential bugs, and add life and depth to your applications through layout, transitions and animations, borders, backgrounds, text properties, and many other tools and techniques.This guide covers:Selectors, specificity, and the cascadeValues, units, fonts, and text propertiesPadding, borders, outlines, and marginsColors, backgrounds, and gradientsFloats and positioning tricksFlexible box layoutThe new Grid layout system2D and 3D transforms, transitions, and animationFilters, blending, clipping, and maskingMedia and feature queries

> > With this book, authors L. Y. Pratt and N. E. Malcolm fill this gap. They present a step-by-step method for integrating technology into decisions that bridge from actions to desired outcomes, with a focus on systems that act in an advisory, human-in-the-loop capacity to decision makers. > How can decision makers use data and technology to ensure desired outcomes? How can technology teams communicate effectively with decision makers to maximize the return on their data and technology investments? How can organizational decision makers assess and improve their decisions over time?

With Learning JavaScript Design Patterns, youll learn how to write beautiful, structured, and maintainable JavaScript by applying classical and modern design patterns to the language. If you want to keep your code efficient, more manageable, and up-to-date with the latest best practices, this book is for you.Explore many popular design patterns, including Modules, Observers, Facades, and Mediators. Learn how modern architectural patternssuch as MVC, MVP, and MVVMare useful from the perspective of a modern web application developer. This book also walks experienced JavaScript developers through modern module formats, how to namespace code effectively, and other essential topics.Learn the structure of design patterns and how they are writtenUnderstand different pattern categories, including creational, structural, and behavioralWalk through more than 20 classical and modern design patterns in JavaScriptUse several options for writing modular codeincluding the Module pattern, Asyncronous Module Definition (AMD), and CommonJSDiscover design patterns implemented in the jQuery libraryLearn popular design patterns for writing maintainable jQuery plug-ins"e;This book should be in every JavaScript developers hands. Its the go-to book on JavaScript patterns that will be read and referenced many times in the future."e;Andre Hansson, Lead Front-End Developer, presis!

Data quality will either make you or break you in the financial services industry. Missing prices, wrong market values, trading violations, client performance restatements, and incorrect regulatory filings can all lead to harsh penalties, lost clients, and financial disaster. This practical guide provides data analysts, data scientists, and data practitioners in financial services firms with the framework to apply manufacturing principles to financial data management, understand data dimensions, and engineer precise data quality tolerances at the datum level and integrate them into your data processing pipelines.You'll get invaluable advice on how to:Evaluate data dimensions and how they apply to different data types and use casesDetermine data quality tolerances for your data quality specificationChoose the points along the data processing pipeline where data quality should be assessed and measuredApply tailored data governance frameworks within a business or technical function or across an organizationPrecisely align data with applications and data processing pipelinesAnd more

Combing the web is simple, but how do you search for data at work? It's difficult and time-consuming, and can sometimes seem impossible. This book introduces a practical solution: the data catalog. Data analysts, data scientists, and data engineers will learn how to create true data discovery in their organizations, making the catalog a key enabler for data-driven innovation and data governance.

Want to learn the Python language without slogging your way through how-to manuals? With Head First Python, youll quickly grasp Pythons fundamentals, working with the built-in data structures and functions. Then youll move on to building your very own webapp, exploring database management, exception handling, and data wrangling. If youre intrigued by what you can do with context managers, decorators, comprehensions, and generators, its all here. This second edition is a complete learning experience that will help you become a bonafide Python programmer in no time.Why does this book look so different? Based on the latest research in cognitive science and learning theory, Head First Pythonuses a visually rich format to engage your mind, rather than a text-heavy approach that puts you to sleep. Why waste your time struggling with new concepts? This multi-sensory learning experience is designed for the way your brain really works.

Get up to speed on Git for tracking, branching, merging, and managing code revisions. Through a series of step-by-step tutorials, this practical guide takes you quickly from Git fundamentals to advanced techniques, and provides friendly yet rigorous advice for navigating the many functions of this open source version control system.This thoroughly revised edition also includes tips for manipulating trees, extended coverage of the reflog and stash, and a complete introduction to the GitHub repository. Git lets you manage code development in a virtually endless variety of ways, once you understand how to harness the systems flexibility. This book shows you how.Learn how to use Git for several real-world development scenariosGain insight into Gits common-use cases, initial tasks, and basic functionsUse the system for both centralized and distributed version controlLearn how to manage merges, conflicts, patches, and diffsApply advanced techniques such as rebasing, hooks, and ways to handle submodulesInteract with Subversion (SVN) repositoriesincluding SVN to Git conversionsNavigate, use, and contribute to open source projects though GitHub

Digital transformation has accelerated nearly tenfold in recent years as both a business and technology journey. Yet, most white papers and how-to guides still focus solely on the business side, rather than include methods for optimizing the technology behind it. This handbook shows CIOs, IT directors, and architects how to balance these two concerns successfully.You'll explore current technology trends and shifts required to build a digital business, including how enterprise architecture should evolve if it's to sustain and grow your business. A CIO who can handle digital transformation along with business interests is a rare find. This is the ideal guide to modernizing IT.You'll examine:The latest trends and technologies driving the need for a digital enterprise architectureNew components, layers, and concepts that comprise a framework for digital enterprise architectureSkills and technologies you need to modernize an enterprise architecture for a digital businessDomains and characteristics of a digital enterprise architectureHow to map digital enterprise technologies to the appropriate teams

Learn how to turn raw data into rich, interactive web visualizations with the powerful combination of Python and JavaScript. With this hands-on guide, author Kyran Dale teaches you how build a basic dataviz toolchain with best-of-breed Python and JavaScript librariesincluding Scrapy, Matplotlib, Pandas, Flask, and D3for crafting engaging, browser-based visualizations.As a working example, throughout the book Dale walks you through transforming Wikipedias table-based list of Nobel Prize winners into an interactive visualization. Youll examine steps along the entire toolchain, from scraping, cleaning, exploring, and delivering data to building the visualization with JavaScripts D3 library. If youre ready to create your own web-based data visualizationsand know either Python or JavaScript this is the book for you.Learn how to manipulate data with PythonUnderstand the commonalities between Python and JavaScriptExtract information from websites by using Pythons web-scraping tools, BeautifulSoup and ScrapyClean and explore data with Pythons Pandas, Matplotlib, and Numpy librariesServe data and create RESTful web APIs with Pythons Flask frameworkCreate engaging, interactive web visualizations with JavaScripts D3 library

Most organizations with a web presence build and operate APIs; the doorway for customers to interact with the company's services. Designing, building, and managing these critical programs affect everyone in the organization, from engineers and product owners to C-suite executives. But the real challenge for developers and solution architects is creating an API platform from the ground up.With this practical book, you'll learn strategies for building and testing REST APIs that use API gateways to combine offerings at the microservice level. Authors James Gough, Daniel Bryant, and Matthew Auburn demonstrate how simple additions to this infrastructure can help engineers and organizations migrate to the cloud; and open the opportunity to connect internal services using technologies like a service mesh.Learn API fundamentals and architectural patterns for building an API platformUse practical examples to understand how to design, build, and test API-based systemsDeploy, operate, and configure key components of an API platformUse API gateways and service meshes appropriately, based on case studiesUnderstand core security and common vulnerabilities in API architectureSecure data and APIs using threat modeling and technologies like OAuth2 and TLSLearn how to evolve existing systems toward API- and cloud-based architectures

This in-depth book shows you ways to master strategic thinking, manage difficult projects, and set the standard for technical work. You'll learn how to be a leader without direct authority, how to plan ahead so that you're making the right technical decisions, and how to make everyone around you better.

This hands-on third edition, expanded and thoroughly updated for version 1.0 and beyond, shows you the fastest way to get up and running with Terraform.

Get going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work.RStudio engineers Max Kuhn and Julia Silge demonstrate ways to create models by focusing on an R dialect called the tidyverse. Software that adopts tidyverse principles shares both a high-level design philosophy and low-level grammar and data structures, so learning one piece of the ecosystem makes it easier to learn the next. You'll understand why the tidymodels framework has been built to be used by a broad range of people.With this book, you will:Learn the steps necessary to build a model from beginning to endUnderstand how to use different modeling and feature engineering approaches fluentlyExamine the options for avoiding common pitfalls of modeling, such as overfittingLearn practical methods to prepare your data for modelingTune models for optimal performanceUse good statistical practices to compare, evaluate, and choose among models

Updated for Python 3.10 and pandas 1.4, the third edition of this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively.

This practical book provides a comprehensive overview of troubleshooting and performance tuning best practices for Microsoft SQL Server. Database engineers, including database developers and administrators, will learn how to identify performance issues, troubleshoot the system in a holistic fashion, and properly prioritize tuning efforts to attain the best system performance possible.Author Dmitri Korotkevitch, Microsoft Data Platform MVP and Microsoft Certified Master (MCM), explains the interdependencies between SQL Server database components. You'll learn how to quickly diagnose your system and discover the root cause of any issue. Techniques in this book are compatible with all versions of SQL Server and cover both on-premises and cloud-based SQL Server installations.Discover how performance issues present themselves in SQL ServerLearn about SQL Server diagnostic tools, methods, and technologiesPerform health checks on SQL Server installationsLearn the dependencies between SQL Server componentsTune SQL Server to improve performance and reduce bottlenecksDetect poorly optimized queries and inefficiencies in query execution plansFind inefficient indexes and common database design issuesUse these techniques with Microsoft Azure SQL databases, Azure SQL Managed Instances, and Amazon RDS for SQL Server

Nowadays, finance, mathematics, and programming are intrinsically linked. This book provides the relevant foundations of each discipline to give you the major tools you need to get started in the world of computational finance.Using an approach where mathematical concepts provide the common background against which financial ideas and programming techniques are learned, this practical guide teaches you the basics of financial economics. Written by the best-selling author of Python for Finance, Yves Hilpisch, Financial Theory with Python explains financial, mathematical, and Python programming concepts in an integrative manner so that the interdisciplinary concepts reinforce each other.Draw upon mathematics to learn the foundations of financial theory and Python programmingLearn about financial theory, financial data modeling, and the use of Python for computational financeLeverage simple economic models to better understand basic notions of finance and Python programming conceptsUse both static and dynamic financial modeling to address fundamental problems in finance, such as pricing, decision-making, equilibrium, and asset allocationLearn the basics of Python packages useful for financial modeling, such as NumPy, pandas, Matplotlib, and SymPy

WebAssembly: The Definitive Guide is a thorough and accessible introduction to one of the most transformative technologies hitting our industry.

Provides a overview of the agile development process that serves as a introduction for non-technical readers.

Based on the latest research in cognitive science and learning theory, Head First Android Development uses a visually rich format to engage your mind, rather than a text-heavy approach that puts you to sleep. Why waste your time struggling with new concepts? This multi-sensory learning experience is designed for the way your brain really works.

Does it seem like your Python projects are getting bigger and bigger? Are you feeling the pain as your codebase expands and gets tougher to debug and maintain? Python is an easy language to learn and use, but that also means systems can quickly grow beyond comprehension. Thankfully, Python has features to help developers overcome maintainability woes.In this practical book, author Patrick Viafore shows you how to use Python's type system to the max. You'll look at user-defined types, such as classes and enums, and Python's type hinting system. You'll also learn how to make Python extensible and how to use a comprehensive testing strategy as a safety net. With these tips and techniques, you'll write clearer and more maintainable code.Learn why types are essential in modern development ecosystemsUnderstand how type choices such as classes, dictionaries, and enums reflect specific intentsMake Python extensible for the future without adding bloatUse popular Python tools to increase the safety and robustness of your codebaseEvaluate current code to detect common maintainability gotchasBuild a safety net around your codebase with linters and tests

With the explosion of data, computing power, and cloud data warehouses, SQL has become an even more indispensable tool for the savvy analyst or data scientist. This practical book reveals new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow.You'll learn how to use both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways--as well as how to combine SQL techniques to accomplish your goals faster, with understandable code. If you work with SQL databases, this is a must-have reference.Learn the key steps for preparing your data for analysisPerform time series analysis using SQL's date and time manipulationsUse cohort analysis to investigate how groups change over timeUse SQL's powerful functions and operators for text analysisDetect outliers in your data and replace them with alternate valuesEstablish causality using experiment analysis, also known as A/B testing

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.You'll discover how to:Apply DevOps best practices to machine learningBuild production machine learning systems and maintain themMonitor, instrument, load-test, and operationalize machine learning systemsChoose the correct MLOps tools for a given machine learning taskRun machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware

Whether you're searching for new or additional opportunities, information security can be vast and overwhelming. In this practical guide, author Christina Morillo introduces technical knowledge from a diverse range of experts in the infosec field. Through 97 concise and useful tips, you'll learn how to expand your skills and solve common issues by working through everyday security problems.You'll also receive valuable guidance from professionals on how to navigate your career within this industry. How do you get buy-in from the C-suite for your security program? How do you establish an incident and disaster response plan? This practical book takes you through actionable advice on a wide variety of infosec topics, including thought-provoking questions that drive the direction of the field.Continuously Learn to Protect Tomorrow's Technology - Alyssa ColumbusFight in Cyber Like the Military Fights in the Physical - Andrew HarrisKeep People at the Center of Your Work - Camille StewartInfosec Professionals Need to Know Operational Resilience - Ann JohnsonTaking Control of Your Own Journey - Antoine MiddletonSecurity, Privacy, and Messy Data Webs: Taking Back Control in Third-Party Environments - Ben BrookEvery Information Security Problem Boils Down to One Thing - Ben SmithFocus on the WHAT and the Why First, Not the Tool - Christina Morillo

What's the answer to today's increasingly complex web applications? Micro-frontends. Inspired by the microservices model, this approach lets you break interfaces into separate features managed by different teams of developers. With this practical guide, Luca Mezzalira shows software architects, tech leads, and software developers how to build and deliver artifacts atomically rather than use a big bang deployment.You'll learn how micro-frontends enable your team to choose any library or framework. This gives your organization technical flexibility and allows you to hire and retain a broad spectrum of talent. Micro-frontends also support distributed or colocated teams more efficiently. Pick up this book and learn how to get started with this technological breakthrough right away.Explore available frontend development architecturesLearn how microservice principles apply to frontend developmentUnderstand the four pillars for creating a successful micro-frontend architectureExamine the benefits and pitfalls of existing micro-frontend architecturesLearn principles and best practices for creating successful automation strategiesDiscover patterns for integrating micro-frontend architectures using microservices or a monolith API layer

This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability.Google engineers Valliappa Lakshmanan, Martin Grner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras.You'll learn how to:Design ML architecture for computer vision tasksSelect a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your taskCreate an end-to-end ML pipeline to train, evaluate, deploy, and explain your modelPreprocess images for data augmentation and to support learnabilityIncorporate explainability and responsible AI best practicesDeploy image models as web services or on edge devicesMonitor and manage ML models

This portable reference to PowerShell summarizes the command shell and scripting language and provides a concise guide to the many tasks that make PowerShell so useful. If you're a busy administrator and don't have time to plow through huge books or in-depth online searches, this is the ideal on-the-job tool.Written by PowerShell team member Lee Holmes and excerpted from his PowerShell Cookbook, this edition offers up-to-date coverage of Windows PowerShell 5.1 and open source PowerShell Core up to 7 and beyond.Beginning with a guided tour of PowerShell, this handy guide covers:PowerShell language and environmentRegular expression referenceXPath quick reference.NET string formatting.NET DateTime formattingSelected .NET classes and their usesWMI referenceSelected COM objects and their usesStandard PowerShell verbs

If you know how to program, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions rather than continuous mathematics.

Packed with code examples, this comprehensive book shows you how to be productive with the language and ecosystem right away. You'll learn why Scala is ideal for building today's highly scalable, data-centric applications, while maximizing developer productivity.

Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges.Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers.Topics include:The Importance of Data Lineage - Julien Le DemData Security for Data Engineers - Katharine JarmulThe Two Types of Data Engineering and Data Engineers - Jesse AndersonSix Dimensions for Picking an Analytical Data Warehouse - Gleb MezhanskiyThe End of ETL as We Know It - Paul SingmanBuilding a Career as a Data Engineer - Vijay KiranModern Metadata for the Modern Data Stack - Prukalpa SankarYour Data Tests Failed! Now What? - Sam Bail

The Lightning Network (LN) is a rapidly growing second-layer payment protocol that works on top of Bitcoin to provide near-instantaneous transactions between two parties. With this practical guide, authors Andreas M. Antonopoulos, Olaoluwa Osuntokun, and Rene Pickhardt explain how this advancement will enable the next level of scale for Bitcoin, increasing speed and privacy while reducing fees.Ideal for developers, systems architects, investors, and entrepreneurs looking to gain a better understanding of LN, this book demonstrates why experts consider LN a critical solution to Bitcoin's scalability problem. You'll learn how LN has the potential to support far more transactions than today's financial networks.This book examines:How the Lightning Network addresses the challenge of blockchain scalingThe Basis of Lightning Technology (BOLT) standards documentsThe five layers of the Lightning Network Protocol SuiteLN basics, including wallets, nodes, and how to operate oneLightning payment channels, onion routing, and gossip protocolFinding paths across payment channels to transport Bitcoin off-chain from sender to recipient