Until now. This practical guide provides the first comprehensive overview of software architecture's many aspects. You'll examine architectural characteristics, architectural patterns, component determination, diagramming and presenting architecture, evolutionary architecture, and many other topics.Authors Neal Ford and Mark Richards help you learn through examples in a variety of popular programming languages, such as Java, C#, JavaScript, and others. You'll focus on architecture principles with examples that apply across all technology stacks.

    This book provides a step-by-step framework on how to tackle a system design question. It includes many real-world examples to illustrate the systematic approach with detailed steps that you can follow.What’s inside?- An insider’s take on what interviewers really look for and why.- A 4-step framework for solving any system design interview question.- 15 real system design interview questions with detailed solutions.- 188 diagrams to visually explain how different systems work.Table Of ContentsChapter 1: Scale From Zero To Millions Of UsersChapter 2: Back-of-the-envelope EstimationChapter 3: A Framework For System Design InterviewsChapter 4: Design A Rate LimiterChapter 5: Design Consistent HashingChapter 6: Design A Key-value StoreChapter 7: Design A Unique Id Generator In Distributed SystemsChapter 8: Design A Url ShortenerChapter 9: Design A Web CrawlerChapter 10: Design A Notification SystemChapter 11: Design A News Feed SystemChapter 12: Design A Chat SystemChapter 13: Design A Search Autocomplete SystemChapter 14: Design YoutubeChapter 15: Design Google DriveChapter 16: The Learning Continues

    With this book, you'll get a candid and insightful look at how software is constructed and maintained by some of the world's leading practitioners.Titus Winters, Tom Manshreck, and Hyrum K. Wright, software engineers and a technical writer at Google, reframe how software engineering is practiced and taught: from an emphasis on programming to an emphasis on software engineering, which roughly translates to programming over time.You'll learn:Fundamental differences between software engineering and programmingHow an organization effectively manages a living codebase and efficiently responds to inevitable changeWhy culture (and recognizing it) is important, and how processes, practices, and tools come into play

    Many Python developers are now taking an interest in high-level software architecture patterns such as hexagonal/clean architecture, event-driven architecture, and strategic patterns prescribed by domain-driven design (DDD). But translating those patterns into Python isn't always straightforward.With this practical guide, Harry Percival and Bob Gregory from MADE.com introduce proven architectural design patterns to help Python developers manage application complexity. Each pattern is illustrated with concrete examples in idiomatic Python that explain how to avoid some of the unnecessary verbosity of Java and C# syntax. You'll learn how to implement each of these patterns in a Pythonic way.Architectural design patterns include:Dependency inversion, and its links to ports and adapters (hexagonal/clean architecture)Domain-driven design's distinction between entities, value objects, and aggregatesRepository and Unit of Work patterns for persistent storageEvents, commands, and the message busCommand Query Responsibility Segregation (CQRS)Event-driven architecture and reactive microservices

    But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications.Authors Jeremy Howard and Sylvain Gugger show you how to train a model on a wide range of tasks using fastai and PyTorch. You'll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes.Train models in computer vision, natural language processing, tabular data, and collaborative filteringLearn the latest deep learning techniques that matter most in practiceImprove accuracy, speed, and reliability by understanding how deep learning models workDiscover how to turn your models into web applicationsImplement deep learning algorithms from scratchConsider the ethical implications of your work

    Industry experts Jacqueline Nolis and Emily Robinson lay out the soft skills you’ll need alongside your technical know-how in order to succeed in the field. Following their clear and simple instructions you’ll craft a resume that hiring managers will love, learn how to ace your interview, and ensure you hit the ground running in your first months at your new job. Once you’ve gotten your foot in the door, learn to thrive as a data scientist by handling high expectations, dealing with stakeholders, and managing failures. Finally, you’ll look towards the future and learn about how to join the broader data science community, leaving a job gracefully, and plotting your career path. With this book by your side you’ll have everything you need to ensure a rewarding and productive role in data science.

    Now introducing Everything You Need to Ace Computer Science and Coding , an essential new title with the potential to run hand-in-hand with—or even outrun—Math (over 1.3 million copies in print) and Science (925,000 copies in print) as the next critical STEM companion.            Instruction is presented in the simple but powerful format of the previous Big Fat Notebooks . The key concepts of coding and computer science easily digested and summarized, with critical ideas clearly explained, doodles that illuminate tricky concepts, and quizzes to recap it all. Kids will explore the concepts of computer science, learn how websites are designed and created, and understand the fundamentals of coding with Scratch, Python, HTML, and CSS.  Written by Grant Smith, a computer science education expert—and vetted by an award-winning computer-science teacher—this  Big Fat Notebook  is for every student who is either taking computer science in school or is a passionate code warrior.

    Learn how to manage this complexity and make sure you keep providing a quality GraphQL API as your team or organization scales.Every language and every GraphQL implementation does things slightly differently. This book is completely language agnostic and instead focuses on concepts and patterns that are achievable no matter how you're building a GraphQL server.Think of it as a complete journey of what goes into building a GraphQL API, from design, to architectures, to implementation, and even documentation.

    

    These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.

    This is so you can go out and do new/novel things with deep learning as well as to become more successful with even more basic models.This book is to accompany the usual free tutorial videos and sample code from youtube.com/sentdex. This topic is one that warrants multiple mediums and sittings. Having something like a hard copy that you can make notes in, or access without your computer/offline is extremely helpful. All of this plus the ability for backers to highlight and post comments directly in the text should make learning the subject matter even easier.

    Don’t try to solve these challenges by yourself—use a framework that handles them for you. Elixir and Phoenix Channels provide a solid foundation on which to build stable and scalable real-time applications. Build applications that thrive for years to come with the best practices found in this book.Understand the magic of real-time communication by inspecting the WebSocket protocol in action. Avoid performance pitfalls early in the development lifecycle with a catalog of common problems and their solutions. Leverage GenStage to build a data pipeline that improves scalability. Break your application before your users do and deploy with confidence. Build a real-world project using solid application design and testing practices that help make future changes a breeze. Create distributed apps that can scale to many users with tools like Phoenix Tracker. Deploy and monitor your application with confidence and reduce outages.Deliver an exceptional real-time experience to your users, with easy maintenance, reduced operational costs, and maximized performance, using Elixir and Phoenix Channels.

    Mastering the contents of this book is a necessary (but admittedly not sufficient) condition if one wishes to take a leap forward from being just another ordinary coder to being among one of the world’s finest competitive programmers. Typical readers of Book 1 (only) of CP4 would include: (1). Secondary or High School Students who are competing in the annual International Olympiad in Informatics (IOI) (including the National or Provincial Olympiads) as Book 1 covers most of the current IOI Syllabus, (2). Casual University students who are using this book as supplementary material for typical Data Structures and Algorithms courses, (3). Anyone who wants to prepare for typical fundamental data structure/algorithm part of a job interview at top IT companies. Typical readers of both Book 1 + Book 2 of CP4 would include: (1). University students who are competing in the annual International Collegiate Programming Contest (ICPC) Regional Contests (including the World Finals) as Book 2 covers much more Computer Science topics that have appeared in the ICPCs, (2). Teachers or Coaches who are looking for comprehensive training materials, (3). Anyone who loves solving problems through computer programs. There are numerous programming contests for those who are no longer eligible for ICPC, including Google CodeJam, Facebook Hacker Cup, TopCoder Open, CodeForces contest, Internet Problem Solving Contest (IPSC), etc.

    Through the course of this hands-on book, you'll build an example ML-driven application from initial idea to deployed product. Data scientists, software engineers, and product managers with little or no ML experience will learn the tools, best practices, and challenges involved in building a real-world ML application step-by-step.Author Emmanuel Ameisen, who worked as a data scientist at Zipcar and led Insight Data Science's AI program, demonstrates key ML concepts with code snippets, illustrations, and screenshots from the book's example application.The first part of this guide shows you how to plan and measure success for an ML application. Part II shows you how to build a working ML model, and Part III explains how to improve the model until it fulfills your original vision. Part IV covers deployment and monitoring strategies.This book will help you:Determine your product goal and set up a machine learning problemBuild your first end-to-end pipeline quickly and acquire an initial datasetTrain and evaluate your ML model and address performance bottlenecksDeploy and monitor models in a production environment

    PyTorch puts these superpowers in your hands, providing a comfortable Python experience that gets you started quickly and then grows with you as you—and your deep learning skills—become more sophisticated. Deep Learning with PyTorch will make that journey engaging and fun.

    What's the next step toward becoming a capable, confident software developer?Welcome to Beyond the Basic Stuff with Python. More than a mere collection of advanced syntax and masterful tips for writing clean code, you'll learn how to advance your Python programming skills by using the command line and other professional tools like code formatters, type checkers, linters, and version control. Sweigart takes you through best practices for setting up your development environment, naming variables, and improving readability, then tackles documentation, organization and performance measurement, as well as object-oriented design and the Big-O algorithm analysis commonly used in coding interviews. The skills you learn will boost your ability to program--not just in Python but in any language.You'll learn: - Coding style, and how to use Python's Black auto-formatting tool for cleaner code - Common sources of bugs, and how to detect them with static analyzers - How to structure the files in your code projects with the Cookiecutter template tool - Functional programming techniques like lambda and higher-order functions - How to profile the speed of your code with Python's built-in timeit and cProfile modules - The computer science behind Big-O algorithm analysis - How to make your comments and docstrings informative, and how often to write them - How to create classes in object-oriented programming, and why they're used to organize codeToward the end of the book you'll read a detailed source-code breakdown of two classic command-line games, the Tower of Hanoi (a logic puzzle) and Four-in-a-Row (a two-player tile-dropping game), and a breakdown of how their code follows the book's best practices. You'll test your skills by implementing the program yourself.Of course, no single book can make you a professional software developer. But Beyond the Basic Stuff with Python will get you further down that path and make you a better programmer, as you learn to write readable code that's easy to debug and perfectly PythonicRequirements: Covers Python 3.6 and higher

    Accomplished pentester and author Phillip L. Wylie and cybersecurity researcher Kim Crawley walk you through the basic and advanced topics necessary to understand how to make a career out of finding vulnerabilities in systems, networks, and applications.You'll learn about the role of a penetration tester, what a pentest involves, and the prerequisite knowledge you'll need to start the educational journey of becoming a pentester. Discover how to develop a plan by assessing your current skillset and finding a starting place to begin growing your knowledge and skills. Finally, find out how to become employed as a pentester by using social media, networking strategies, and community involvement.Perfect for IT workers and entry-level information security professionals, The Pentester BluePrint also belongs on the bookshelves of anyone seeking to transition to the exciting and in-demand field of penetration testing.Written in a highly approachable and accessible style, The Pentester BluePrint avoids unnecessarily technical lingo in favor of concrete advice and practical strategies to help you get your start in pentesting. This book will teach you:The foundations of pentesting, including basic IT skills like operating systems, networking, and security systems The development of hacking skills and a hacker mindset Where to find educational options, including college and university classes, security training providers, volunteer work, and self-study Which certifications and degrees are most useful for gaining employment as a pentester How to get experience in the pentesting field, including labs, CTFs, and bug bounties

    However, this convenience opens your systems to new security risks. API Security in Action gives you the skills to build strong, safe APIs you can confidently expose to the world. Inside, you’ll learn to construct secure and scalable REST APIs, deliver machine-to-machine interaction in a microservices architecture, and provide protection in resource-constrained IoT (Internet of Things) environments.

    "If you intend to use machine learning to solve business problems at scale, I'm delighted you got your hands on this book."—Cassie Kozyrkov, Chief Decision Scientist at Google"Foundational work about the reality of building machine learning models in production."—Karolis Urbonas, Head of Machine Learning and Science at Amazon

    But how can you process such varied workloads efficiently? Enter Apache Spark.Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you'll be able to:Learn Python, SQL, Scala, or Java high-level Structured APIsUnderstand Spark operations and SQL EngineInspect, tune, and debug Spark operations with Spark configurations and Spark UIConnect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or KafkaPerform analytics on batch and streaming data using Structured StreamingBuild reliable data pipelines with open source Delta Lake and SparkDevelop machine learning pipelines with MLlib and productionize models using MLflow

    

    Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow.The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the most appropriate remedy for your situation.You’ll learn how to:Identify and mitigate common challenges when training, evaluating, and deploying ML modelsRepresent data for different ML model types, including embeddings, feature crosses, and moreChoose the right model type for specific problemsBuild a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuningDeploy scalable ML systems that you can retrain and update to reflect new dataInterpret model predictions for stakeholders and ensure that models are treating users fairly

    Gaming AI suggests that this belief is both dumb and self-defeating. Displaying a profound and crippling case of professional amnesia, the computer science establishment shows an ignorance of the most important findings of its own science, from Kurt G�del's "incompleteness" to Alan Turing's "oracle" to Claude Shannon's "entropy." Dabbling in quantum machines, these believers in machine transcendence defy the deepest findings of quantum theory. Claiming to create minds, they are clinically "out of their minds." Despite the quasi-religious pretensions of techno-elites nobly saving the planet from their own devices, their faith in a techno-utopian singularity is a serious threat to real progress. An industry utterly dependent on human minds will not prosper by obsoleting both their customers and their creators. Gaming AI calls for a remedial immersion in the industry's own heroic history and an understanding of the actual science of their own human minds.

    

    Readers learn how to use key deep learning algorithms without the need for complex math.Deep Learning algorithms can start with mountains of data and measurements and turn them into useful and meaningful patterns. This book is for people with sharp minds who may lack the math background necessary to deal with equations or complex mechanics, but who nevertheless want to understand the "how" of deep learning, and actually use these tools for themselves.Deep Learning: A Visual Approach helps demystify the algorithms that enable computers to drive cars, win chess tournaments, and create symphonies, while giving readers the tools necessary to build their own systems to help them find the information hiding within their own data, create "deep dream" artwork, or create new stories in the style of their favorite authors. Scientists, artists, programmers, managers, hobbyists, and intellectual adventurers of all kinds can use deep learning tools to make new discoveries and create new kinds of art and intelligent systems.The book's friendly, informal approach to deep learning demonstrates the concepts visually. There's no math beyond the occasional multiplication and no programming experience is required. By the end of the book, readers will be equipped to understand modern deep learning systems, and anyone who wants to program and train their own deep learning networks will be able to dive into the library of their choice and start implementing with knowledge and confidence.

    Intriguing projects teach readers how to tackle challenging problems with code.Computer programming is about solving real problems with code. Real World Python is a collection of worked projects for readers who know some basic Python and want to do something with their knowledge. The book's short projects all teach thought processes and problem-solving as well as coding syntax. Readers learn to think their way through challenges like predicting the location of sailors lost at sea, discovering new planets, determining the author of a novel, selecting candidate landing sites for a Mars rover, programming a robot sentry gun to detect and shoot aliens (not humans), and more. The book should appeal to younger learners and mature readers, especially scientists and engineers looking to increase their Python skills. Most chapters give the reader a role (NASA intern, Coast Guard Director of Operations, linguistic detective, UN diplomatic associate, Aliens movie franchise Colonial Marine, and so on). Vaughan walks readers through planning and implementing solutions to complex problems. The book's various projects introduce important Python modules, like NLTK and OpenCV, which are used extensively in data analysis and machine learning. By the end of the book readers will be able to think through complex Python projects and have the tools necessary to tackle them.

    

    Application areas range from search engines to tournament scheduling, DNA sequencing, and machine learning. Arguing that every educated person today needs to have some understanding of algorithms and what they do, in this volume in the MIT Press Essential Knowledge series, Panos Louridas offers an introduction to algorithms that is accessible to the nonspecialist reader. Louridas explains not just what algorithms are but also how they work, offering a wide range of examples and keeping mathematics to a minimum.After discussing what an algorithm does and how its effectiveness can be measured, Louridas covers three of the most fundamental applications areas: graphs, which describe networks, from eighteenth-century problems to today's social networks; searching, and how to find the fastest way to search; and sorting, and the importance of choosing the best algorithm for particular tasks. He then presents larger-scale applications: PageRank, Google's founding algorithm; and neural networks and deep learning. Finally, Louridas describes how all algorithms are nothing more than simple moves with pen and paper, and how from such a humble foundation rise all their spectacular achievements.

    The core of AI is the algorithms that the system uses to do things like identifying objects in an image, interpreting the meaning of text, or looking for patterns in data to spot fraud and other anomalies.  Mastering the core algorithms for search, image recognition, and other common tasks is essential to building good AI applications Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts.You’ll explore coding challenges like detect­ing bank fraud, creating artistic masterpieces, and setting a self-driving car in motion. All you need is the algebra you remember from high school math class and beginning programming skills.  What You Will Learn Use cases for different AI algorithms Intelligent search for decision making Biologically inspired algorithms Machine learning and neural networks Reinforcement learning to build a better robot This Book Is Written For For software developers with high school–level math skills. About the Author Rishal Hurbans is a technologist, startup and AI group founder, and international speaker. Table of Contents 1 Intuition of artificial intelligence 2 Search fundamentals 3 Intelligent search 4 Evolutionary algorithms 5 Advanced evolutionary approaches 6 Swarm intelligence: Ants 7 Swarm intelligence: Particles 8 Machine learning 9 Artificial neural networks 10 Reinforcement learning with Q-learning

    With PowerShell, you can automate tasks with scripts without having to learn the complicated ins and outs of programming. After you familiarize yourself with PowerShell's intuitive syntax, you'll apply your knowledge by designing and developing scripts for lots of daily situations IT personnel find themselves in every day. You'll then end with learning how to build a large project to automate server deployments from scratch written completely in PowerShell.In Part One, you'll be brought up to an intermediate knowledge level of PowerShell by exploring the ins and outs of core concepts with PowerShell's intuitive syntax.In Part Two, you'll begin to apply your knowledge and develop various scripts to automate all kinds of real-world tasks that a tech professional may run into.Finally, in Part Three, you will take all of that knowledge you learned in the first two parts and begin work on a large PowerShell module called PowerLab. In this Part, you will build a real-world project purely in PowerShell that will teach you that PowerShell isn't just for a few scripts here and there! This PowerLab module will enable you to automatically create Hyper-V virtual machines, install Windows servers, and provision domain controllers, IIS web servers, and SQL servers.With this book's vast amount of real-world, applicable examples coming from an author who's lived PowerShell for over 8 years, you'll uncover hundreds of tactics and techniques that even the most seasoned PowerShell expert may not know. Unlock the possibilities with PowerShell!

    

    They provide a set of proven solutions that allow developers to solve problems in software development quickly. This book will demonstrate how to leverage design patterns with real-world applications.Starting with an overview of design patterns and best practices in application design, you'll learn about some of the most fundamental Julia features such as modules, data types, functions/interfaces, and metaprogramming. You'll then get to grips with the modern Julia design patterns for building large-scale applications with a focus on performance, reusability, robustness, and maintainability. The book also covers anti-patterns and how to avoid common mistakes and pitfalls in development. You'll see how traditional object-oriented patterns can be implemented differently and more effectively in Julia. Finally, you'll explore various use cases and examples, such as how expert Julia developers use design patterns in their open source packages.By the end of this Julia programming book, you'll have learned methods to improve software design, extensibility, and reusability, and be able to use design patterns efficiently to overcome common challenges in software development. What you will learn Master the Julia language features that are key to developing large-scale software applications Discover design patterns to improve overall application architecture and design Develop reusable programs that are modular, extendable, performant, and easy to maintain Weigh up the pros and cons of using different design patterns for use cases Explore methods for transitioning from object-oriented programming to using equivalent or more advanced Julia techniques Who this book is for This book is for beginner to intermediate-level Julia programmers who want to enhance their skills in designing and developing large-scale applications. Table of Contents Design Patterns and Related Principles Modules, Packages, and Data Type Concepts Designing Functions and Interfaces Macros and Meta Programming Techniques Reusability Patterns Performance Patterns Maintainability Patterns Robustness Patterns Miscellaneous Patterns Anti-Patterns Object Oriented Traditional Patterns Inheritance and Variance

    So in this short book, we will help you build a mental model of how SwiftUI works. We explain the most important concepts in detail, and we follow them up with exercises to give you hands-on experience.SwiftUI is still a young framework, and as such, we don’t believe it’s appropriate to write a complete reference. Instead, this book focuses on transitioning your way of thinking from the object-oriented style of UIKit to the declarative style of SwiftUI.Thinking in SwiftUI is geared toward readers who are familiar with Swift and who have experience building apps in frameworks like UIKit.

    Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether.Summary The beating heart of any product or service business is returning clients. Don't let your hard-won customers vanish, taking their money with them. In Fighting Churn with Data you'll learn powerful data-driven techniques to maximize customer retention and minimize actions that cause them to stop engaging or unsubscribe altogether. This hands-on guide is packed with techniques for converting raw data into measurable metrics, testing hypotheses, and presenting findings that are easily understandable to non-technical decision makers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Keeping customers active and engaged is essential for any business that relies on recurring revenue and repeat sales. Customer turnover—or “churn”—is costly, frustrating, and preventable. By applying the techniques in this book, you can identify the warning signs of churn and learn to catch customers before they leave. About the book Fighting Churn with Data teaches developers and data scientists proven techniques for stopping churn before it happens. Packed with real-world use cases and examples, this book teaches you to convert raw data into measurable behavior metrics, calculate customer lifetime value, and improve churn forecasting with demographic data. By following Zuora Chief Data Scientist Carl Gold’s methods, you’ll reap the benefits of high customer retention. What's inside     Calculating churn metrics     Identifying user behavior that predicts churn     Using churn reduction tactics with customer segmentation     Applying churn analysis techniques to other business areas     Using AI for accurate churn forecasting About the reader For readers with basic data analysis skills, including Python and SQL. About the author Carl Gold (PhD) is the Chief Data Scientist at Zuora, Inc., the industry-leading subscription management platform. Table of Contents: PART 1 - BUILDING YOUR ARSENAL 1 The world of churn 2 Measuring churn 3 Measuring customers 4 Observing renewal and churn PART 2 - WAGING THE WAR 5 Understanding churn and behavior with metrics 6 Relationships between customer behaviors 7 Segmenting customers with advanced metrics PART 3 - SPECIAL WEAPONS AND TACTICS 8 Forecasting churn 9 Forecast accuracy and machine learning 10 Churn demographics and firmographics 11 Leading the fight against churn

    Securing the messages, queues, and API endpoints require new approaches to security both in the infrastructure and the code. Microservices Security in Action teaches you how to address microservices-specific security challenges throughout the system. This practical guide includes plentiful hands-on exercises using industry-leading open-source tools and examples using Java and Spring Boot.

    This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback.Summary We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of deep reinforcement learning: building machine learning systems that explore and learn based on the responses of the environment. Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You'll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology We learn by interacting with our environment, and the rewards or punishments we experience guide our future behavior. Deep reinforcement learning brings that same natural process to artificial intelligence, analyzing results to uncover the most efficient ways forward. DRL agents can improve marketing campaigns, predict stock performance, and beat grand masters in Go and chess. About the book Grokking Deep Reinforcement Learning uses engaging exercises to teach you how to build deep learning systems. This book combines annotated Python code with intuitive explanations to explore DRL techniques. You’ll see how algorithms function and learn to develop your own DRL agents using evaluative feedback. What's inside     An introduction to reinforcement learning     DRL agents with human-like behaviors     Applying DRL to complex situations About the reader For developers with basic deep learning experience. About the author Miguel Morales works on reinforcement learning at Lockheed Martin and is an instructor for the Georgia Institute of Technology’s Reinforcement Learning and Decision Making course. Table of Contents 1 Introduction to deep reinforcement learning 2 Mathematical foundations of reinforcement learning 3 Balancing immediate and long-term goals 4 Balancing the gathering and use of information 5 Evaluating agents’ behaviors 6 Improving agents’ behaviors 7 Achieving goals more effectively and efficiently 8 Introduction to value-based deep reinforcement learning 9 More stable value-based methods 10 Sample-efficient value-based methods 11 Policy-gradient and actor-critic methods 12 Advanced actor-critic methods 13 Toward artificial general intelligence

    Monitoring the health and performance of these distributed architectures requires a new approach. Enter distributed tracing, a method of profiling and monitoring applications--especially those that use microservice architectures. There's just one problem: distributed tracing can be hard. But it doesn't have to be.With this practical guide, you'll learn what distributed tracing is and how to use it to understand the performance and operation of your software. Key players at LightStep walk you through instrumenting your code for tracing, collecting the data that your instrumentation produces, and turning it into useful, operational insights. If you want to start implementing distributed tracing, this book tells you what you need to know.You'll learn:The pieces of a distributed tracing deployment: Instrumentation, data collection, and delivering valueBest practices for instrumentation (the methods for generating trace data from your service)How to deal with or avoid overhead, costs, and samplingHow to work with spans (the building blocks of request-based distributed traces) and choose span characteristics that lead to valuable tracesWhere distributed tracing is headed in the future

    For each problem that a programmer wants to solve, they employ an algorithm: a sequence of steps for solving the problem. Many books teach algorithms independently of specific problems, but this book uses careful explanations, examples, and arguments, rather than formal mathematics and proofs which make it difficult for the reader to connect what they are learning to what they can do with that learning. Algorithmic Thinking: A Problem-Based Introduction teaches the reader to use the best algorithms and data structures for a given situation by walking them through solving real-world problems pulled from international programming competitions, such as how to determine whether snowflakes are unique; how to win a game in the minimum number of moves; how to find the number of ways to get to someone's house; how to escape a cave in as few steps as possible; and so on.Readers tackle challenging topics like recursion, dynamic programming, graphs, greedy algorithms, heaps, hash tables, segment trees, and other data structures for efficiently handling data. The book contains no pseudocode: all code is written in C and is thoroughly explained in the text (C is a de facto programming language for programming competitions). Zingaro also shows how several problems can be reduced to algorithms on graphs. By the end of the book, readers should understand the importance of modeling, how to carefully work through a problem, and why it pays to organize data using data structures.

    Well, let me surprise you! Linux is simple and easy to learn, and this book is the ultimate proof!You may have stumbled across a variety of sources that all explain Linux in a complicated and dry manner. This book does exactly the opposite; it teaches you Linux in a delightful and friendly way so that you will never get bored, and you will always feel motivated to learn more.Learn Linux Quickly doesn't assume any prior Linux knowledge, which makes it a perfect fit for beginners. Nevertheless, intermediate and advanced Linux users will still find this book very useful as it goes through a wide range of topics. Learn Linux Quickly will teach you the following topics:Installing LinuxOver 116 Linux CommandsUser and Group ManagementLinux Networking FundamentalsBash ScriptingAutomate Boring Tasks with Cron JobsCreate your Own Linux CommandsLinux Disk Partitioning and LVMFinding Files on LinuxUnderstanding File PermissionsLinux ProcessesAnd much more! There is no time to waste here! Learn Linux Quickly and kick start your Linux career today!

    Practical advice that does exist usually assumes that your team already has the infrastructure, tooling, and culture in place. In this book, recognized SLO expert Alex Hidalgo explains how to build an SLO culture from the ground up.Ideal as a primer and daily reference for anyone creating both the culture and tooling necessary for SLO-based approaches to reliability, this guide provides detailed analysis of advanced SLO and service-level indicator (SLI) techniques. Armed with mathematical models and statistical knowledge to help you get the most out of an SLO-based approach, you'll learn how to build systems capable of measuring meaningful SLIs with buy-in across all departments of your organization.Define SLIs that meaningfully measure the reliability of a service from a user's perspectiveChoose appropriate SLO targets, including how to perform statistical and probabilistic analysisUse error budgets to help your team have better discussions and make better data-driven decisionsBuild supportive tooling and resources required for an SLO-based approachUse SLO data to present meaningful reports to leadership and your users

    

    The reason? Bad data semantics.In this practical and comprehensive field guide, author Panos Alexopoulos takes you on an eye-opening journey through semantic data modeling as applied in the real world. You'll learn how to master this craft to increase the usability and value of your data and applications. You'll also explore the pitfalls to avoid and dilemmas to overcome for building high-quality and valuable semantic representations of data.Understand the fundamental concepts, phenomena, and processes related to semantic data modelingExamine the quirks and challenges of semantic data modeling and learn how to effectively leverage the available frameworks and toolsAvoid mistakes and bad practices that can undermine your efforts to create good data modelsLearn about model development dilemmas, including representation, expressiveness and content, development, and governanceOrganize and execute semantic data initiatives in your organization, tackling technical, strategic, and organizational challenges

    Once used chiefly to add interactivity to web browser windows, JavaScript is now a primary building block of powerful and robust applications. In this practical book, new and experienced JavaScript developers will learn how to use this language to create APIs as well as web, mobile, and desktop applications.Author and engineering leader Adam D. Scott covers technologies such as Node.js, GraphQL, React, React Native, and Electron. Ideal for developers who want to build full stack applications and ambitious web development beginners looking to bootstrap a startup, this book shows you how to create a single CRUD-style application that will work across several platforms.Explore GraphQL's simple process for querying dataLearn about shared authentication for APIs, web apps, and native applicationsBuild performant web applications with React and Styled ComponentsUse React Native to write cross-platform applications for iOS and Android that compile to native codeLearn how to write desktop applications with Electron

    With this hands-on guide, Java engineers will learn how to use their language experience in the new world of serverless computing. You'll discover how this cloud computing model can drastically reduce the complexity in developing and operating applications while reducing costs and time to market.Engineering leaders John Chapin and Mike Roberts guide you through the process of developing serverless applications using AWS Lambda, Amazon's event-driven, serverless computing platform. You'll learn how to prepare the development environment, program Lambda functions, and deploy and operate your serverless software. Chapters includes exercises to help you through each aspect of the process.Get an introduction to serverless, functions-as-a-service, and AWS LambdaLearn how to deploy working Lambda functions to the cloudProgram Lambda functions and learn how to get data in and outBuild and package Java-based Lambda code and dependenciesCreate serverless applications by building a serverless API and data pipelineTest your serverless applications using automated techniquesApply advanced techniques to build production-ready applications

    After an introduction to functional programming languages, we present propositional logic, λ-calculus, the Curry-Howard correspondence, first-order logic, Agda, dependent types and homotopy type theory.

    A technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce comparisons of shape to a comparison of algebraic invariants, such as numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolutionary biology as well as mathematicians interested in applied topology.

    

    

    

    Nowadays, our lives are run by algorithms. They determine what news we see. They influence which products we buy. They suggest our dating partners. They may even be determining the outcome ofnational elections. They are creating, and destroying, entire industries. Despite mounting concerns, few know what algorithms are, how they work, or who created them.Poems that Solve Puzzles tells the story of algorithms from their ancient origins to the present day and beyond. The book introduces readers to the inventors and inspirational events behind the genesis of the world's most important algorithms. Professor Chris Bleakley recounts tales of ancient lostinscriptions, Victorian steam-driven contraptions, top secret military projects, penniless academics, hippy dreamers, tech billionaires, superhuman artificial intelligences, cryptocurrencies, and quantum computing. Along the way, the book explains, with the aid of clear examples and illustrations, how the most influential algorithms work.Compelling and impactful, Poems that Solve Puzzles tells the story of how algorithms came to revolutionise our world.

    This book is a result of a quest. A quest to uncover what habits can be cultivated to become a better software engineer.

    

    

    This edition has been updated to cover 64-bit machines, newer peripheral devices, larger memory systems, large-scale SSDs, and newer CPUs like those used in personal computers and tablets.

    Community building must be “hacked.” In Hacking Communities, Laís de Oliveira examines how we can renew our shared sense of belonging. Drawing on her own personal struggle with loneliness, as well as academic research and her professional experience in building communities with nonprofits, startups, and public organizations, she provides frameworks and methodologies to build stronger and more diverse communities. Hacking Communities aims to empower anyone to start and grow a community, making the case that everyone is a potential community builder.

    Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.

    This second edition of the highly-regarded Thinking Low-Level, Writing High-Level (Volume 2 in the best-selling Write Great Code series) teaches readers how to produce better machine code by directing the actions of their chosen compiler.This second edition has been updated to cover high-level programming languages (such as Swift and Java) as well as code generation on 64-bit CPUsARM, the Java Virtual Machine, and the Microsoft Common Runtime.

    But in practice, individual microservices can inadvertently impact others and alter the end user experience. Effective microservices architectures require standardization on an organizational level with the help of a platform engineering team.This practical book provides a series of progressive steps that platform engineers can apply technically and organizationally to achieve highly resilient Java applications. Author Jonathan Schneider covers many effective SRE practices from companies leading the way in microservices adoption. You'll examine several patterns discovered through much trial and error in recent years, complete with Java code examples.Chapters are organized according to specific patterns, including:Application metrics: Monitoring for availability with MicrometerDebugging with observability: Logging and distributed tracing; failure injection testingCharting and alerting: Building effective charts; KPIs for Java microservicesSafe multicloud delivery: Spinnaker, deployment strategies, and automated canary analysisSource code observability: Dependency management, API utilization, and end-to-end asset inventoryTraffic management: Concurrency of systems; platform, gateway, and client-side load balancing

    

    In the beginning, there may be little or no data to work with. At this point, a basic solution that uses rule based systems or traditional machine learning will be apt. As you accumulate more data, more sophisticated--and often data intensive--ML techniques can be used including deep learning. At each step of this journey, there are dozens of alternative approaches you can take. This book helps you navigate this maze of options.

    Readers dig deep into technical (and related legal) issues, as they learn what kinds of devices to use as hacking tools and which make the best targets. The authors, all experts in the field, cover the kinds of vulnerabilities found in IoT devices, explain how to exploit their network protocols, and how to leverage security flaws and certain hardware interfaces found in the physical devices themselves.The book begins with threat modeling and a security testing methodology, then covers how to attack hardware interfaces such as UART, I�C, SPI, JTAG / SWD and IoT network protocols like UPnP, WS-Discovery, mDNS, DNS-SD, RTSP / RTCP / RTP, LoRa / LoRaWAN, Wi-Fi / Wi-Fi Direct, RFID / NFC, BLE, MQTT, CDP and DICOM. Examples throughout offer custom code designed to demonstrate specific vulnerabilities and tools to help readers reproduce the attacks. Practical IoT Hacking is full of practical exercises and hands-on examples taken from the authors' own research that teach readers things like how to bypass the authentication of an STM32F103 device (black pill) through SWD; reverse firmware; exploit zero-configuration networking; use low-cost equipment to capture LoRa network traffic; analyze IoT companion mobile apps, take over and remotely control an Android based treadmill, jam wireless devices such as home alarm systems, hijack Bluetooth Low Energy connections and how to circumvent modern RFID and NFC enabled smart door locks.

    First, we will walk through the entire process of writing an article, from pitch to content, editing to publication. Next, we will explore the process in action with three full sample articles. Finally, we will discuss the business of writing and how to turn your newfound confidence in the article- writing process into a profitable side gig or even full-time employment. Along the way, we will hear from successful members of the software/writing community about their best practices. These excerpts are lightly edited for readability. Full transcripts of each interview are available in Appendix A, and I encourage you to read all eleven complete interviews as they are loaded with valuable insights.

    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. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Deep reinforcement learning AI systems rapidly adapt to new environments, a vast improvement over standard neural networks. A DRL agent learns like people do, taking in raw data such as sensor input and refining its responses and predictions through trial and error. About the book Deep Reinforcement Learning in Action teaches you how to program AI agents that adapt and improve based on direct feedback from their environment. In this example-rich tutorial, you’ll master foundational and advanced DRL techniques by taking on interesting challenges like navigating a maze and playing video games. Along the way, you’ll work with core algorithms, including deep Q-networks and policy gradients, along with industry-standard tools like PyTorch and OpenAI Gym. What's inside     Building and training DRL networks     The most popular DRL algorithms for learning and problem solving     Evolutionary algorithms for curiosity and multi-agent learning     All examples available as Jupyter Notebooks About the reader For readers with intermediate skills in Python and deep learning. About the author Alexander Zai is a machine learning engineer at Amazon AI. Brandon Brown is a machine learning and data analysis blogger. Table of Contents PART 1 - FOUNDATIONS 1. What is reinforcement learning? 2. Modeling reinforcement learning problems: Markov decision processes 3. Predicting the best states and actions: Deep Q-networks 4. Learning to pick the best policy: Policy gradient methods 5. Tackling more complex problems with actor-critic methods PART 2 - ABOVE AND BEYOND 6. Alternative optimization methods: Evolutionary algorithms 7. Distributional DQN: Getting the full story 8.Curiosity-driven exploration 9. Multi-agent reinforcement learning 10. Interpretable reinforcement learning: Attention and relational models 11. In conclusion: A review and roadmap

    

    Designing APIs with Swagger and OpenAPI is a hands-on primer to properly designing and describing your APIs using the most widely-adopted standard. Designing APIs with Swagger and OpenAPI introduces you to a design-first paradigm that will teach you the best practices for describing and designing RESTful APIs using OpenAPI and Swagger. You’ll build upon progressively-enhanced examples as you learn to describe an API and then extend it in the kind of scenarios you’d encounter in the real world. As you go, you’ll use the popular Open Source tools to define APIs, generate documentation, and build other developer-friendly components like mocks, server stubs, and client SDKs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

    

    The book starts with the basics, including mean square, least squares and maximum likelihood methods, ridge regression, Bayesian decision theory classification, logistic regression, and decision trees. It then progresses to more recent techniques, covering sparse modelling methods, learning in reproducing kernel Hilbert spaces and support vector machines, Bayesian inference with a focus on the EM algorithm and its approximate inference variational versions, Monte Carlo methods, probabilistic graphical models focusing on Bayesian networks, hidden Markov models and particle filtering. Dimensionality reduction and latent variables modelling are also considered in depth.This palette of techniques concludes with an extended chapter on neural networks and deep learning architectures. The book also covers the fundamentals of statistical parameter estimation, Wiener and Kalman filtering, convexity and convex optimization, including a chapter on stochastic approximation and the gradient descent family of algorithms, presenting related online learning techniques as well as concepts and algorithmic versions for distributed optimization.Focusing on the physical reasoning behind the mathematics, without sacrificing rigor, all the various methods and techniques are explained in depth, supported by examples and problems, giving an invaluable resource to the student and researcher for understanding and applying machine learning concepts. Most of the chapters include typical case studies and computer exercises, both in MATLAB and Python.The chapters are written to be as self-contained as possible, making the text suitable for different courses: pattern recognition, statistical/adaptive signal processing, statistical/Bayesian learning, as well as courses on sparse modeling, deep learning, and probabilistic graphical models.New to this edition:Complete re-write of the chapter on Neural Networks and Deep Learning to reflect the latest advances since the 1st edition. The chapter, starting from the basic perceptron and feed-forward neural networks concepts, now presents an in depth treatment of deep networks, including recent optimization algorithms, batch normalization, regularization techniques such as the dropout method, convolutional neural networks, recurrent neural networks, attention mechanisms, adversarial examples and training, capsule networks and generative architectures, such as restricted Boltzman machines (RBMs), variational autoencoders and generative adversarial networks (GANs).Expanded treatment of Bayesian learning to include nonparametric Bayesian methods, with a focus on the Chinese restaurant and the Indian buffet processes.

    

    

    From a clear explanation of Heawood’s disproof of Kempe’s argument to novel features like quadrilateral switching, this book by Chris McMullen, Ph.D., is packed with content. It even includes a novel handwaving argument explaining why the four-color theorem is true. What is the four-color theorem? Why is it common to work with graphs instead of maps? What are Kempe chains? What is the problem with Alfred Kempe’s attempted proof? How does Euler’s formula relate the numbers of faces, edges, and vertices? What are Kuratowski’s theorem and Wagner’s theorem? What is the motivation behind triangulation? What is quadrilateral switching? What is vertex splitting? What is the three-edges theorem? Is there an algorithm for four-coloring a map or graph? What is a Hamiltonian cycle? What is a separating triangle? How is the four-color theorem like an ill-conditioned logic puzzle? Why is the four-color theorem true? What makes the four-color theorem so difficult to prove by hand?

    

    

    Python is the ideal language for network engineers to explore tools that were previously available to systems engineers and application developers. In Mastering Python Networking, Third edition, you’ll embark on a Python-based journey to transition from traditional network engineers to network developers ready for the next-generation of networks.This new edition is completely revised and updated to work with Python 3. In addition to new chapters on network data analysis with ELK stack (Elasticsearch, Logstash, Kibana, and Beats) and Azure Cloud Networking, it includes updates on using newer libraries such as pyATS and Nornir, as well as Ansible 2.8. Each chapter is updated with the latest libraries with working examples to ensure compatibility and understanding of the concepts.Starting with a basic overview of Python, the book teaches you how it can interact with both legacy and API-enabled network devices. You will learn to leverage high-level Python packages and frameworks to perform network automation tasks, monitoring, management, and enhanced network security followed by Azure and AWS Cloud networking. Finally, you will use Jenkins for continuous integration as well as testing tools to verify your network. What you will learn Use Python libraries to interact with your network Integrate Ansible 2.8 using Python to control Cisco, Juniper, and Arista network devices Leverage existing Flask web frameworks to construct high-level APIs Learn how to build virtual networks in the AWS & Azure Cloud Learn how to use Elastic Stack for network data analysis Understand how Jenkins can be used to automatically deploy changes in your network Use PyTest and Unittest for Test-Driven Network Development in networking engineering with Python Who this book is for Mastering Python Networking, Third edition is for network engineers, developers, and SREs who want to use Python for network automation, programmability, and data analysis. Basic familiarity with Python programming and networking-related concepts such as Transmission Control Protocol/Internet Protocol (TCP/IP) will be useful. Table of Contents Review of TCP/IP Protocol Suite and Python Low-Level Network Device Interactions APIs and Intent-Driven Networking The Python Automation Framework – Ansible Basics The Python Automation Framework – Beyond Basics Network Security with Python Network Monitoring with Python – Part 1 Network Monitoring with Python – Part 2 Building Network Web Services with Python AWS Cloud Networking Azure Cloud Networking Network Data Analysis with Elastic Stack Working with Git

    Now, as part of its promise to bring revolutionary change in untold ways to human activity, artificial intelligence—AI—is about to create another complete transformation in how companies create and deliver value to customers. But despite the billions spent so far on bots and other tools, AI continues to stumble. Why can't it magically use all the data organizations generate to make them run faster and better? Because something is missing.AI works only when it understands the soul of the business. An ontology is a holistic digital model of every piece of information that matters to the business, from processes to products to people, and it's what makes the difference between the promise of AI and delivering on that promise.Business leaders who want to catch the AI wave—rather than be crushed by it—need to read The AI-Powered Enterprise. The book is the first to combine a sophisticated explanation of how AI works with a practical approach to applying AI to the problems of business, from customer experience to business operations to product development.

    By imitating the evolutionary process, genetic algorithms can overcome hurdles encountered in traditional search algorithms and provide high-quality solutions for a variety of problems. This book will help you get to grips with a powerful yet simple approach to applying genetic algorithms to a wide range of tasks using Python, covering the latest developments in artificial intelligence.After introducing you to genetic algorithms and their principles of operation, you'll understand how they differ from traditional algorithms and what types of problems they can solve. You'll then discover how they can be applied to search and optimization problems, such as planning, scheduling, gaming, and analytics. As you advance, you'll also learn how to use genetic algorithms to improve your machine learning and deep learning models, solve reinforcement learning tasks, and perform image reconstruction. Finally, you'll cover several related technologies that can open up new possibilities for future applications.By the end of this book, you'll have hands-on experience of applying genetic algorithms in artificial intelligence as well as in numerous other domains. What you will learn Understand how to use state-of-the-art Python tools to create genetic algorithm-based applications Use genetic algorithms to optimize functions and solve planning and scheduling problems Enhance the performance of machine learning models and optimize deep learning network architecture Apply genetic algorithms to reinforcement learning tasks using OpenAI Gym Explore how images can be reconstructed using a set of semi-transparent shapes Discover other bio-inspired techniques, such as genetic programming and particle swarm optimization Who this book is for This book is for software developers, data scientists, and AI enthusiasts who want to use genetic algorithms to carry out intelligent tasks in their applications. Working knowledge of Python and basic knowledge of mathematics and computer science will help you get the most out of this book. Table of Contents An Introduction to Genetic Algorithms Understanding the Key Components of Genetic Algorithms Using the DEAP Framework Combinatorial Optimization Constraint Satisfaction Optimizing Continuous Functions Enhancing Machine Learning Models Using Feature Selection Hyperparameter Tuning Machine Learning Models Architecture Optimization of Deep Learning Networks Reinforcement Learning with Genetic Algorithms Genetic Image Reconstruction Other Evolutionary and Bio-Inspired Computation Techniques

    

    The lessons in this book will supercharge your career by sharing lessons and mistakes from real developers.Wouldn't it be nice to learn from others' career mistakes? "Soft" skills are crucial to success, but are haphazardly picked up on the job or, worse, never learned. Understanding these competencies and how to improve them will make you a more effective team member and a more attractive hire.This book will teach you the key skills you need, including how to ask questions, how and when to use common tools, and how to interact with other team members. Each will be presented in context and from multiple perspectives so you'll be able to integrate them and apply them to your own career quickly.What You'll LearnKnow when the best code is no codeUnderstand what to do in the first month of your jobSee the surprising number of developers who can't programAvoid the pitfalls of working aloneWho This Book Is ForAnyone who is curious about software development as a career choice. You have zero to five years of software development experience and want to learn non-technical skills that can help your career. It is also suitable for teachers and mentors who want to provide guidance to their students and/or mentees.

    Network pioneer Silvano Gai demonstrates DS Platforms’ remarkable capabilities and guides you through implementing them in diverse hardware. Focusing on business benefits throughout, Gai shows how to provide essential shared services such as segment routing, NAT, firewall, micro-segmentation, load balancing, SSL/TLS termination, VPNs, RDMA, and storage—including storage compression and encryption. He also compares three leading hardware-based approaches—Sea of Processors, FPGAs, and ASICs—preparing you to evaluate solutions, ask the right questions, and plan strategies for your environment. Understand the business drivers behind DS Platforms, and the value they offer See how modern network design and virtualization create a foundation for DS Platforms Achieve unprecedented scale through domain-specific hardware, standardized functionalities, and granular distribution Compare advantages and disadvantages of each leading hardware approach to DS Platforms Learn how P4 Domain-Specific Language and architecture enable high-performance, low-power ASICs that are data-plane-programmable at runtime Distribute cloud security services, including firewalls, encryption, key management, and VPNs Implement distributed storage and RDMA services in large-scale cloud networks Utilize Distributed Services Cards to offload networking processing from host CPUs Explore the newest DS Platform management architectures Building a Future-Proof Cloud Architecture is for network, cloud, application, and storage engineers, security experts, and every technology professional who wants to succeed with tomorrow’s most advanced service architectures.

    Now in its 9th edition, you will find the latest C# 8 and .NET Core features, along with new chapters on Microsoft's lightweight, cross-platform framework, .NET Core 3.1. Coverage of ASP.NET Core, Entity Framework Core, and more, sits alongside the latest updates to .NET Core, including Windows Presentation Foundation (WPF). Not only does this book cover all of the latest features in C# 8, but all chapters and code samples have been rewritten for this latest release.Dive in and discover why this book has been a favorite of C# developers worldwide for more than 15 years. Gain a solid foundation in object-oriented development techniques, attributes and reflection, generics and collections, and numerous advanced topics not found in other texts (such as CIL opcodes and emitting dynamic assemblies). With the help of Pro C# 8 with .NET Core 3 gain the confidence to put C# into practice and explore the .NET Core universe on your own terms.What You Will LearnDiscover the bells and whistles of C# 8 features and updates to previous featuresHit the ground running with ASP.NET Core web applications and web services, and Entity Framework CoreWork with the latest version of Windows Presentation Foundation, now a part of .NET CoreUnderstand the philosophy behind .NET and the cross-platform alternative, .NET CoreDevelop applications with C# and modern frameworks for services, web, and smart client applicationsWho This Book Is ForDevelopers who are interested in .NET programming and the C# language"Amazing! Provides easy-to-follow explanations and examples. I remember reading the first version of this book; this is a 'must-have' for your collection if you are learning .NET Core!" - Rick McGuire, Senior Application Development Manager, Microsoft"Phil is a journeyman programmer who brings years of experience and a passion for teaching to make this fully revised and modernized 'classic' a 'must-have'. Any developer who wants full-spectrum, up-to-date coverage of both the C# language and how to use it with .NET Core and ASP.NET Core should get this book."- Brian A. Randell, Partner, MCW Technologies and Microsoft MVP

    The basic purpose of flowcharting is to create the sequence of steps for showing the solution to problems through arithmetic and/or logical manipulations used to instruct computers. The applied and illustrative examples from different subject areas will definitely encourage readers to learn the logic leading to solid programming basics.Features: -Uses flowcharts and algorithms to solve problems from everyday applications, teaching the logic needed for the creation of computer instructions-Covers arrays, looping, file processing, etc.

    

    

    Given its enormous impact on everyday life, it is essential that its debated origins are understood, and that its different foundations are explained. On the Foundations of Computing offers a comprehensive and critical overview of the birth and evolution of computing, and it presents some of the most important technical results and philosophical problems of the discipline, combining both historical and systematic analyses.The debates this text surveys are among the latest and most urgent ones: the crisis of foundations in mathematics and the birth of the decision problem, the nature of algorithms, the debates on computational artefacts and malfunctioning, and the analysis of computational experiments. By covering these topics, On the Foundations of Computing provides a much-needed resource to contextualize these foundational issues.For practitioners, researchers, and students alike, a historical and philosophical approach such as what this volume offers becomes essential to understand the past of the discipline and to figure out the challenges of its future.

    This third edition of Framework Design Guidelines adds guidelines related to changes that the .NET team adopted during transition from the world of client-server application to the world of the Cloud." -From the Foreword by Scott Guthrie Framework Design Guidelines has long been the definitive guide to best practices for developing components and component libraries in Microsoft .NET. Now, this third edition has been fully revised to reflect game-changing API design innovations introduced by Microsoft through eight recent updates to C#, eleven updates to .NET Framework, and the emergence and evolution of .NET Core.Three leading .NET architects share the same guidance Microsoft teams are using to evolve .NET, so you can design well-performing components that feel like natural extensions to the platform. Building on the book's proven explanatory style, the authors and expert annotators offer insider guidance on new .NET and C# concepts, including major advances in asynchronous programming and lightweight memory access. Throughout, they clarify and refresh existing content, helping you take full advantage of best practices based on C# 8, .NET Framework 4.8, and .NET Core. Discover which practices should always, generally, rarely, or never be used-including practices that are no longer recommended Learn the general philosophy and fundamental principles of modern framework design Explore common framework design patterns with up-to-date C# examples Apply best practices for naming, types, extensibility, and exceptions Learn how to design libraries that scale in the cloud Master new async programming techniques utilizing Task and ValueTask Make the most of the Memory and Span types for lightweight memory access This guide is an indispensable resource for everyone who builds reusable .NET-based frameworks, libraries, or components at any scale: large system frameworks, medium-size reusable layers of large distributed systems, extensions to system frameworks, or even small shared components.Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

    Computer Graphics from Scratch aims to demystify these algorithms and show readers that computer graphics can be surprisingly simple. This broad introductory book gives readers an overview of the computer graphics field with a focus on two core areas of modern graphics: raytracing and rasterization. Links to interactive demos throughout bring the algorithms alive. Every algorithm is built up without the use of external libraries or APIs and is presented with language agnostic pseudocode, allowing anyone with a basic understanding of programming and high school math to follow along.

    

    While discounted in some circles as derivative and consigned to perpetual catch-up mode, China's hybrid system of legal, illegal, and extralegal import of foreign technology, combined with its indigenous efforts, is, the authors believe, enormously effective and must be taken seriously. Accordingly, in this volume, 17 international specialists combine their scholarship to portray the system's structure and functioning in heretofore unseen detail, using primary Chinese sources to demonstrate the perniciousness of the problem in a manner not likely to be controverted. The book concludes with a series of recommendations culled from the authors' interactions with experts worldwide.This book will be of much interest to students of Chinese politics, US foreign policy, intelligence studies, science and technology studies, and International Relations in general.

    These principles are presented using Haskell, a purely functional language, leading to simpler explanations and shorter programs than would be obtained with imperative languages. Carefully selected examples, both new and standard, reveal the commonalities and highlight the differences between algorithms. The algorithm developments use equational reasoning where applicable, clarifying the applicability conditions and correctness arguments. Every chapter concludes with exercises (nearly 300 in total), each with complete answers, allowing the reader to consolidate their understanding and apply the techniques to a range of problems. The book serves students (both undergraduate and postgraduate), researchers, teachers, and professionals who want to know more about what goes into a good algorithm and how such algorithms can be expressed in purely functional terms.

    The primary focus is on the Linux booting procedure along with other popular operating systems such as Windows and Unix.Hands-on Booting begins by explaining what a bootloader is, starting with the Linux bootloader followed by bootloaders for Windows and Unix systems. Next, you'll address the BIOS and UEFI firmware by installing multiple operating systems on one machine and booting them through the Linux bootloader. Further, you'll see the kernel's role in the booting procedure of the operating system and the dependency between kernel, initramfs, and dracut. You'll also cover systemd, examining its structure and how it mounts the user root filesystem. In the final section, the book explains troubleshooting methodologies such as debugging shells followed by live images and rescue mode.On completing this book, you will understand the booting process of major operating systems such as Linux, Windows, and Unix. You will also know how to fix the Linux booting issues through various boot modes.What You Will LearnExamine the BIOS and UEFI firmwareUnderstanding the Linux boot loader (GRUB)Work with initramfs, dracut, and systemdFix can't-boot issues on LinuxWho This Book Is For Linux users, administrators, and developers.

    Designing life-saving drugs and solving super-large logistics problems that are difficult or impossible for classical computers to handle are now within reach. Instead of iterating through each possible configuration one by one, quantum computing speeds up the search by giving you the tools to examine all possibilities simultaneously to find those that work. Now you can work with real quantum computers instead of just talking about them theoretically.Discover qubits, the fundamental elements of quantum computing. Learn what makes them different from classical bits. Model complex problems with logic gates specifically tailored for quantum computing. See how quantum mechanics offers ways to write programs that examine all solutions simultaneously. Create quantum circuits to solve problems that classical computers struggle with. Dive into quantum optimization and cryptography. Use the IBM Q quantum computer to both simulate quantum effects and actually run your programs on a real quantum machine.Get a head start on the technology that will drive computer science into the future.

    Whether you're new to web development or new only to Rails, Ruby on Rails(TM) Tutorial, Sixth Edition , is the solution.Best-selling author and leading Rails developer Michael Hartl teaches Rails by guiding you through the development of three example applications of increasing sophistication. The tutorial's examples focus on the general principles of web development needed for virtually any kind of website. The updates to this edition include full compatibility with Rails 6.This indispensable guide provides integrated tutorials not only for Rails, but also for the essential Ruby, HTML, CSS, and SQL skills you need when developing web applications. Hartl explains how each new technique solves a real-world problem, and then he demonstrates it with bite-sized code that's simple enough to understand while still being useful. Whatever your previous web development experience, this book will guide you to true Rails mastery.This book will help youInstall and set up your Rails development environment, including a pre-installed integrated development environment (IDE) in the cloud Go beyond generated code to truly understand how to build Rails applications from scratch Learn testing and test-driven development (TDD) Effectively use the Model-View-Controller (MVC) pattern Structure applications using the REST architecture Build static pages and transform them into dynamic ones Master the Ruby programming skills all Rails developers need Create high-quality site layouts and data models Implement registration and authentication systems, including validation and secure passwords Update, display, and delete users Upload images in production using a cloud storage service Implement account activation and password reset, including sending email with Rails Add social features and microblogging, including an introduction to Ajax Record version changes with Git and create a secure remote repository at GitHub Deploy your applications early and often with Heroku Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

    

    The text now contains new examples and material highlighting the emergence of mobile computing and the cloud. It explores this generational change with updated content ...Download a copy here : readbux.com/download?i=0124077269            0124077269 Computer Organization and Design MIPS Edition: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design) PDF by David A. PattersonRead Computer Organization and Design MIPS Edition: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design) PDF from Morgan Kaufmann,David A. PattersonDownload David A. Patterson's PDF E-book Computer Organization and Design MIPS Edition: The Hardware/Software Interface (The Morgan Kaufmann Series in Computer Architecture and Design)

    

    This comprehensive, full-color guide is the only book you need to learn ASP.NET Core development.Professional developers get ready to produce leaner applications for the ASP.NET Core platform. This edition puts ASP.NET Core 3 into context, and takes a deep dive into the tools and techniques required to build modern, extensible web applications. New features and capabilities such as MVC 3, Razor Pages, Blazor Server, and Blazor WebAssembly are covered, along with demonstrations of how they can be applied in practice.Following the same popular format and style found in previous editions, author Adam Freeman explains how to get the most out of ASP.NET Core 3. Starting with the nuts-and-bolts topics, he teaches readers about middleware components, built-in services, request model binding, and more. Moving along, he introduces increasingly more complex topics and advanced features, including endpoint routing and dependency injection.Written for professionals wanting to incorporate the latest functionality of ASP.NET Core 3 into their projects, this book also serves as a complete reference on ASP.NET Core. Beginners with some background in Microsoft web development will also greatly benefit from the in-depth coverage provided throughout.What You Will LearnBuild a solid foundation and skill set for working with the entire ASP.NET Core platformApply ASP.NET Core 3 and ASP.NET Core 5 features in your developer environment; plentiful reusable templatesSee how to create RESTful web services, web applications, and client-side applicationsLeverage existing knowledge to efficiently get up and running with new programming modelsAdam Freeman is an experienced IT professional who has held senior positions in a range of companies, most recently serving as chief technology officer and chief operating officer of a global bank. Now retired, he spends his time writing and long-distance running."The Rolls-Royce of ASP.NET books, (or if you're American, the Cadillac). Very thorough!"Les Jackson, MCSD, DotNet Playbook"The author's instruction is direct, easy to understand and supplemented with clear code examples... Whether you are a beginner learning ASP.NET Core 3.1 app development, or an experienced professional ready to master advanced concepts, I consider this book a 'must have' for you!"Jeremy Likness, Senior Program Manager, Microsoft"...the best single resource for teaching MVC web apps using ASP.NET. "Charles Carter, MSCS, MSwE, JD, Cloud Application Development Instructor, Microsoft Software and Systems Academy

    By learning how to build and deploy scalable model pipelines, data scientists can own more of the model production process and more rapidly deliver data products. This book provides a hands-on approach to scaling up Python code to work in distributed environments in order to build robust pipelines. Readers will learn how to set up machine learning models as web endpoints, serverless functions, and streaming pipelines using multiple cloud environments. It is intended for analytics practitioners with hands-on experience with Python libraries such as Pandas and scikit-learn, and will focus on scaling up prototype models to production. From startups to trillion dollar companies, data science is playing an important role in helping organizations maximize the value of their data. This book helps data scientists to level up their careers by taking ownership of data products with applied examples that demonstrate how to: Translate models developed on a laptop to scalable deployments in the cloud Develop end-to-end systems that automate data science workflows Own a data product from conception to production The accompanying Jupyter notebooks provide examples of scalable pipelines across multiple cloud environments, tools, and libraries (github.com/bgweber/DS_Production). Book Contents Here are the topics covered by Data Science in Production: Chapter 1: Introduction - This chapter will motivate the use of Python and discuss the discipline of applied data science, present the data sets, models, and cloud environments used throughout the book, and provide an overview of automated feature engineering. Chapter 2: Models as Web Endpoints - This chapter shows how to use web endpoints for consuming data and hosting machine learning models as endpoints using the Flask and Gunicorn libraries. We'll start with scikit-learn models and also set up a deep learning endpoint with Keras. Chapter 3: Models as Serverless Functions - This chapter will build upon the previous chapter and show how to set up model endpoints as serverless functions using AWS Lambda and GCP Cloud Functions. Chapter 4: Containers for Reproducible Models - This chapter will show how to use containers for deploying models with Docker. We'll also explore scaling up with ECS and Kubernetes, and building web applications with Plotly Dash. Chapter 5: Workflow Tools for Model Pipelines - This chapter focuses on scheduling automated workflows using Apache Airflow. We'll set up a model that pulls data from BigQuery, applies a model, and saves the results. Chapter 6: PySpark for Batch Modeling - This chapter will introduce readers to PySpark using the community edition of Databricks. We'll build a batch model pipeline that pulls data from a data lake, generates features, applies a model, and stores the results to a No SQL database. Chapter 7: Cloud Dataflow for Batch Modeling - This chapter will introduce the core components of Cloud Dataflow and implement a batch model pipeline for reading data from BigQuery, applying an ML model, and saving the results to Cloud Datastore. Chapter 8: Streaming Model Workflows - This chapter will introduce readers to Kafka and PubSub for streaming messages in a cloud environment.