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The Raspberry Pi is a credit card-sized computer that plugs into your TV and a keyboard. It is a capable little computer which can be used in electronics projects, and for many of the things that your desktop PC does, like spreadsheets, word processing, browsing the internet, and playing games. It also plays high-definition video.

Give your organization the data protection it deserves without the uncertainty and cost overruns experienced by your predecessors or other companies. System and network administrators have their work cut out for them to protect physical and virtual machines in the data center and the cloud; mobile devices including laptops and tablets; SaaS services like Microsoft 365, Google Workspace, and Salesforce; and persistent data created by Kubernetes and container workloads.To help you navigate the breadth and depth of this challenge, this book presents several solutions so you can determine which is right for your company. You'll learn the unique requirements that each workload presents, then explore various categories of commercial backup hardware, software, and services available to protect these data sources, including the advantages and disadvantages of each approach.Learn the workload types that your organization should be backing upExplore the hardware, software, and services you can use to back up your systemsUnderstand what's wrong with your current data protection systemPair your backed-up workloads to the appropriate backup systemLearn the adjustments that will make your backups better, without wasting money

Given the demand for AI and the ubiquity of JavaScript, TensorFlow.js was inevitable. With this Google framework, seasoned AI veterans and web developers alike can help propel the future of AI-driven websites. In this guide, author Gant Laborde--Google Developer Expert in machine learningand the web--provides a hands-on end-to-end approach to TensorFlow.js fundamentals for a broad technical audience that includes data scientists, engineers, web developers, students, and researchers.You'll begin by working through some basic examples in TensorFlow.js before diving deeper into neural network architectures, DataFrames, TensorFlow Hub, model conversion, transfer learning, and more. Once you finish this book, you'll know how to build and deploy production-readydeep learning systems with TensorFlow.js.Explore tensors, the most fundamental structure of machine learningConvert data into tensors and back with a real-world exampleCombine AI with the web using TensorFlow.jsUse resources to convert, train, and manage machine learning dataBuild and train your own training models from scratch

With this book, data scientists from the Python and R communities will learn how to speak the dialects of each language. By recognizing the strengths of working with both, you'll discover new ways to accomplish data science tasks and expand your skill set.

Distils years of hard-won experience into practical cut-and-paste solutions to everyday Linux dilemmas. Use just one recipe from this collection of real-world solutions, and the hours of tedious trial-and-error saved will more than pay for the cost of the book.

This hands-on guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. You'll learn how to combine small, yet powerful, command-line tools to quickly obtain, scrub, explore, and model your data.

In this practical book, author Nikhil Buduma provides examples and clear explanations to guide you through major concepts of this complicated field. If you're familiar with Python, and have a background in calculus, along with a basic understanding of machine learning, this book will get you started.

Blockchain technology continues to disrupt a wide variety of organizations, from small businesses to the Fortune 500. Today hundreds of blockchain networks are in production, including many built with Hyperledger Fabric. This practical guide shows developers how the latest version of this blockchain infrastructure provides an ideal foundation for developing enterprise blockchain applications or solutions.Authors Matt Zand, Xun Wu, and Mark Anthony Morris demonstrate how the versatile design of Hyperledger Fabric 2.0 satisfies a broad range of industry use cases. Developers with or without previous Hyperledger experience will discover why no other distributed ledger technology framework enjoys such wide adoption by cloud service providers such as Amazon, Alibaba, IBM, Google, and Oracle.Walk through the architecture and components of Hyperledger Fabric 2.0Migrate your current Hyperledger Fabric projects to version 2.0Develop blockchain applications on the Hyperledger platform with Node.jsDeploy and integrate Hyperledger on Amazon Managed Blockchain, IBM Cloud, and Oracle CloudDevelop blockchain applications with Hyperledger Aries, Avalon, Besu, and GridBuild end-to-end blockchain supply chain applications with Hyperledger

Kubernetes has become an essential part of the daily work for most system, network, and cluster administrators today. But to work effectively together on a production-scale Kubernetes system, they must be able to speak the same language. This book provides a clear guide to the layers of complexity and abstraction that come with running a Kubernetes network.Authors James Strong and Vallery Lancey bring you up to speed on the intricacies that Kubernetes has to offer for large container deployments. If you're to be effective in troubleshooting and maintaining a production cluster, you need to be well versed in the abstraction provided at each layer. This practical book shows you how.Learn the Kubernetes networking modelChoose the best interface for your clusters from the CNCF Container Network Interface projectExplore the networking and Linux primitives that power KubernetesQuickly troubleshoot networking issues and prevent downtimeExamine cloud networking and Kubernetes using the three major providers: Amazon Web Services, Google Cloud, and Microsoft AzureLearn the pros and cons of various network tools--and how to select the best ones for your stack

Features information on vim, the vi clone. This guide helps you learn text editing basics and advanced tools for both editors, such as multi-window editing, how to write both interactive macros and scripts to extend the editor, and power tools for programmers. It also includes an introduction to text editing with vi.

With this practical book, AI and machine learning practitioners will learn how to successfully build and deploy data science projects on Amazon Web Services. The Amazon AI and machine learning stack unifies data science, data engineering, and application development to help level upyour skills. This guide shows you how to build and run pipelines in the cloud, then integrate the results into applications in minutes instead of days. Throughout the book, authors Chris Fregly and Antje Barth demonstrate how to reduce cost and improve performance.Apply the Amazon AI and ML stack to real-world use cases for natural language processing, computer vision, fraud detection, conversational devices, and moreUse automated machine learning to implement a specific subset of use cases with SageMaker AutopilotDive deep into the complete model development lifecycle for a BERT-based NLP use case including data ingestion, analysis, model training, and deploymentTie everything together into a repeatable machine learning operations pipelineExplore real-time ML, anomaly detection, and streaming analytics on data streams with Amazon Kinesis and Managed Streaming for Apache KafkaLearn security best practices for data science projects and workflows including identity and access management, authentication, authorization, and more

This concise, easy-to-use reference puts one of the most popular frameworks for deep learning research and development at your fingertips. Author Joe Papa provides instant access to syntax, design patterns, and code examples to accelerate your development and reduce the time you spend searching for answers.Research scientists, machine learning engineers, and software developers will find clear, structured PyTorch code that covers every step of neural network development??from loading data to customizing training loops to model optimization and GPU/TPU acceleration. Quickly learn how to deploy your code to production using AWS, Google Cloud, or Azure and deploy your ML models to mobile and edge devices.Learn basic PyTorch syntax and design patternsCreate custom models and data transformsTrain and deploy models using a GPU and TPUTrain and test a deep learning classifierAccelerate training using optimization and distributed trainingAccess useful PyTorch libraries and the PyTorch ecosystem

Developing applications for the Android mobile operating system can seem daunting, particularly if it requires learning a new programming language: Kotlin, now Android??s officialdevelopment language. With this practical book, Android developers will learn how to make the transition from Java to Kotlin, including how Kotlin provides a true advantage for gaining control over asynchronous computations.Authors Pierre-Olivier Laurence, Amanda Hinchman-Dominguez, G. Blake Meike, and Mike Dunn explore implementations of the most common tasks in native Android development, and show you how Kotlin can help you solve concurrency problems. With a focus on structured concurrency, a new asynchronous programming paradigm, this book will guide you through one of Kotlin's most powerful constructs, coroutines.Learn about Kotlin essentials and the Kotlin Collections FrameworkExplore Android fundamentals: the operating system and the application container and its componentsLearn about thread safety and how to handle concurrencyWrite sequential, asynchronous work at a low costExamine structured concurrency with coroutines, and learn how channels make coroutines communicateLearn how to use flows for asynchronous data processingUnderstand performance considerations using Android profiling toolsUse performance optimizations to trim resource consumption

If you want to increase Tableau's value to your organization, this practical book has your back. Authors Ann Jackson and Luke Stanke guide data analysts through recipes for solving real-world analytics problems using Tableau.

In a crowded field of data visualization and analytics tools, Tableau Desktop has emerged as the clear leader. This is partly due to its ease of use, but once you dive into Tableau's extensive feature set, you'll understand just how powerful and flexible this software can be for your business or organization.With this handy pocket reference, author Ryan Sleeper (Innovative Tableau) shows you how to translate the vast amounts of data into useful information. Tableau has done an amazing job of making valuable insights accessible to analysts and executives who would otherwise need to rely on IT. This book quickly guides you through Tableau Desktop's learning curve.You'll learn:How to shape data for use with Tableau DesktopHow to create the most effective chart typesCore concepts including discrete versus continuousMust-know technical features including filters, parameters, and setsKey syntax for creating the most useful analysesHow to bring it all together with dashboardsAnd more!

Kubernetes has become the dominant container orchestrator, but many organizations that have recently adopted this system are still struggling to run actual production workloads. In this practical book, four software engineers from VMware bring their shared experiences running Kubernetes in production and provide insight on key challenges and best practices.The brilliance of Kubernetes is how configurable and extensible the system is, from pluggable runtimes to storage integrations. For platform engineers, software developers, infosec, network engineers, storage engineers, and others, this book examines how the path to success with Kubernetes involves a variety of technology, pattern, and abstraction considerations.With this book, you will:Understand what the path to production looks like when using KubernetesExamine where gaps exist in your current Kubernetes strategyLearn Kubernetes's essential building blocks--and their trade-offsUnderstand what's involved in making Kubernetes a viable location for applicationsLearn better ways to navigate the cloud native landscape

Learn how to program the Internet of Things with this hands-on guide. By breaking down IoT programming complexities in step-by-step, building-block fashion, author and educator Andy King shows you how to design and build your own full-stack, end-to-end IoT solution--from device to cloud.

With the immense cost savings and scalability the cloud provides, the rationale for building cloud native applications is no longer in question. The real issue is how. With this practical guide, developers will learn about the most commonly used design patterns for building cloud native applications using APIs, data, events, and streams in both greenfield and brownfield development.You'll learn how to incrementally design, develop, and deploy large and effective cloud native applications that you can manage and maintain at scale with minimal cost, time, and effort. Authors Kasun Indrasiri and Sriskandarajah Suhothayan highlight use cases that effectively demonstrate the challenges you might encounter at each step.Learn the fundamentals of cloud native applicationsExplore key cloud native communication, connectivity, and composition patternsLearn decentralized data management techniquesUse event-driven architecture to build distributed and scalable cloud native applicationsExplore the most commonly used patterns for API management and consumptionExamine some of the tools and technologies you'll need for building cloud native systems

Harness the full power of the behavioral data in your company by learning tools specifically designed for behavioral data analysis. Common data science algorithms and predictive analytics tools treat customer behavioral data, such as clicks on a website or purchases in a supermarket, the same as any other data. Instead, this practical guide introduces powerful methods specifically tailored for behavioral data analysis.Advanced experimental design helps you get the most out of your A/B tests, while causal diagrams allow you to tease out the causes of behaviors even when you can't run experiments. Written in an accessible style for data scientists, business analysts, and behavioral scientists, thispractical book provides complete examples and exercises in R and Python to help you gain more insight from your data--immediately.Understand the specifics of behavioral dataExplore the differences between measurement and predictionLearn how to clean and prepare behavioral dataDesign and analyze experiments to drive optimal business decisionsUse behavioral data to understand and measure cause and effectSegment customers in a transparent and insightful way

Life scientists today urgently need training in bioinformatics skills. Too many bioinformatics programs are poorly written and barely maintained--usually by students and researchers who've never learned basic programming skills. This practical guide shows postdoc bioinformatics professionals and students how to exploit the best parts of Python to solve problems in biology while creating documented, tested, reproducible software.Ken Youens-Clark, author of Tiny Python Projects (Manning), demonstrates not only how to write effective Python code but also how to use tests to write and refactor scientific programs. You'll learn the latest Python features and tools??including linters, formatters, type checkers, and tests??to create documented and tested programs. You'll also tackle 14 challenges in Rosalind, a problem-solving platform for learning bioinformatics and programming.Create command-line Python programs to document and validate parametersWrite tests to verify refactor programs and confirm they're correctAddress bioinformatics ideas using Python data structures and modules such as BiopythonCreate reproducible shortcuts and workflows using makefilesParse essential bioinformatics file formats such as FASTA and FASTQFind patterns of text using regular expressionsUse higher-order functions in Python like filter(), map(), and reduce()

The book is written in a casual, conversational style. It is easily accessible to those who have no prior knowledge in 3D printing, yet the book's message is solidly practical, technically accurate, and consumer-relevant. The chapters include contemporary, real-life learning exercises and insights for how to buy, use and maintain 3D printers.

Digital fabrication is a cornerstone of the maker community, and in Make: Volume 75 (our diamond issue!), we take a fun look at the state of digifab today. In our cover story, we break out of the cartesian box with a look at Nick Sewardâ??s weird and wild 3D printer designs, from undulating arms to roving printers. Next, dive into the latest desktop mills, routers and 3D printers with reviews of machines from Bantam Tools, Prusa Research, and more. Plus, get tips for recycling your old prints, adding supports for resin prints, printing in clay, and more.Plus, over 16 projects and skill builders, including:Fit real AI onto microcontroller with TinyML and ArduinoBuild a super efficient rocket stove out of tin cans for your next camping tripBeam streaming services to your TV using a Raspberry Pi 4Create an adorable light-up felted mushroom gardenAnd much more!

What do Docker, Kubernetes, and Prometheus have in common? All of these cloud native technologies are written in the Go programming language. This practical book shows you how to use Go's strengths to develop cloud native services that are scalable and resilient, even in an unpredictable environment.

Tell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with simple drag-and-drop tools such as Google Sheets, Datawrapper, and Tableau Public. You'll also gradually learn how to edit open source code templates like Chart.js, Highcharts, and Leaflet on GitHub.Hands-On Data Visualization takes you step-by-step through tutorials, real-world examples, and online resources. This practical guide is ideal for students, nonprofit organizations, small business owners, local governments, journalists, academics, and anyone who wants to take data out of spreadsheets and turn it into lively interactive stories. No coding experience is required.Build interactive charts and maps and embed them in your websiteUnderstand the principles for designing effective charts and mapsLearn key data visualization concepts to help you choose the right toolsConvert and transform tabular and spatial data to tell your data storyEdit and host Chart.js, Highcharts, and Leaflet map code templates on GitHubLearn how to detect bias in charts and maps produced by others

Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft.Dive into Kubeflow architecture and learn best practices for using the platformUnderstand the process of planning your Kubeflow deploymentInstall Kubeflow on an existing on-premises Kubernetes clusterDeploy Kubeflow on Google Cloud Platform step-by-step from the command lineUse the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWSDeploy and manage Kubeflow across a network of Azure cloud data centers around the worldUse KFServing to develop and deploy machine learning models

Node.js is used by many companies for building performant backend services without sacrificing developer efficiency. In this hands-on guide, author Thomas Hunter II proves that Node.js is just as capable as traditional enterprise platforms for building services that are observable, scalable, and resilient.

Welcome to Make: 's 2020 Guide to Boards! This year brings powerful new releases from Adafruit, Arduino, BeagleBoard, Google, Nvidia, Raspberry Pi, Teensy, and more. We've assembled the technical data for new boards and returning favorites (listing over 50% more than our previous guide!), along with highlighting options to consider for your next project. To further help your decision-making process, some of our favorite electronics experts explain how they pick the right board for their projects. But the real star of electronic prototyping in 2020 is the software: In our cover story, we look at how Python-powered boards make it easier than ever to code for hardware. Plus, your favorite YouTube makers offer their tips and tricks for getting started making videos, how to grow your channel, and what you need to get a great shot (Hint: it's probably in your pocket right now). And don't forget, Halloween is right around the corner! Learn to build an R/C roving pop-up zombie-in-a-trashcan to scare the daylights out of the neighborhood, make a light and cheap fog projection screen for your haunted house, and create a flaming window setup so realistic, you'll have to warn the fire department about erroneous reports ahead of time. Lastly, read the finale of Make: 's series on how our community can help avert catastrophic climate change. Plus, over 43 projects including: Block ads across your entire home network with the Pi-hole ad blockerUse code to make beautiful topographical maps of the Moon, Mars, and moreCreate a swirling stormy snow globe with LED-lit rheoscopic fluidBuild a DIY mobile handwashing station for your communityTips and builds for exercising your constitutional right to protest safely and effectivelyMacrame an adorable Bay Yoda from The MandalorianAnd much more!

Tap into the wisdom of experts to learn what every UX practitioner needs to know. With 97 short and extremely useful articles, you'll discover new approaches to old problems, pick up road-tested best practices, and hone your skills through sound advice.Working in UX involves much more than just creating user interfaces. UX teams struggle with understanding what's important, which practices they should know deeply, and what approaches aren't helpful at all. With these 97 concise articles, editor Dan Berlin presents a wealth of advice and knowledge from experts who have practiced UX throughout their careers.Bring Themes to Exploratory Research--Shanti KanhaiDesign for Content First--Marli MesibovDesign for Universal Usability--Ann Chadwick-DiasBe Wrong on Purpose--Skyler Ray TaylorDiverse Participant Recruiting Is Critical to Authentic User Research--Megan CamposPut On Your InfoSec Hat to Improve Your Designs--Julie MeridianBoost Your Emotional Intelligence to Move from Good to Great UX--Priyama Barua

This hands-on guide helps you get up to speed on the latest and most promising trends in NLP. With some Python experience and a basic understanding of machine learning, you'll learn how to build and deploy real-world NLP applications in your organization.

As you move data to the cloud, you need to consider a comprehensive approach to data governance, along with well-defined and agreed-upon policies to ensure your organization meets compliance requirements. Data governance incorporates the ways people, processes, and technology work together to ensure data is trustworthy and can be used effectively. This practical guide shows you how to effectively implement and scale data governance throughout your organization.Chief information, data, and security officers and their teams will learn strategy and tooling to support democratizing data and unlocking its value while enforcing security, privacy, and other governance standards. Through good data governance, you can inspire customer trust, enable your organization to identify business efficiencies, generate more competitive offerings, and improve customer experience. This book shows you how.You'll learn:Data governance strategies addressing people, processes, and toolsBenefits and challenges of a cloud-based data governance approachHow data governance is conducted from ingest to preparation and useHow to handle the ongoing improvement of data qualityChallenges and techniques in governing streaming dataData protection for authentication, security, backup, and monitoringHow to build a data culture in your organization