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In Software Telemetry you will learn how to:Manage toxic telemetry and confidential recordsMaster multi-tenant techniques and transformation processesUpdate to improve the statistical validity of your metrics and dashboardsMake software telemetry emissions easier to parseBuild easily-auditable logging systemsPrevent and handle accidental data leaksMaintain processes for legal complianceJustify increased spend on telemetry softwareSoftware Telemetry teaches you best practices for operating andupdating telemetry systems. These vital systems trace, log, and monitor infrastructure by observing and analysing the events generated by the system.This practical guide is filled with techniques you can apply to any size of organization, with troubleshooting techniques for every eventuality, and methods to ensureyour compliance with standards like GDPR.

From its humble beginnings a container orchestration system, Kubernetes has become the de facto infrastructure for cloud native applications. Kubernetes impacts every aspect of the application development lifecycle, from design through deployment. To build and operate reliable cloud native systems, you need to understand whats going on below the surface. Core Kubernetes is packed with experience-driven insights and practical techniques, and takes you inside Kubernetes to teach you what youll need to know to keep your system running like a well-oiled machine and prevent those panicked 3 AM phone calls.

Learn how to think about your development pipeline as amission-critical application, with techniques for implementing code-driven infrastructure and CI/CD systems using Jenkins, Docker, Terraform, andcloud-native services. In Pipeline as Code, you will master: Building and deploying a Jenkins cluster from scratch Writing pipeline as code for cloud native applications Automating the deployment of Dockerized and Serverless applications Containerizing applications with Docker and Kubernetes Deploying Jenkins on AWS, GCP and Azure Managing, securing and monitoring a Jenkins cluster in production Key principles for a successful DevOps culture Pipeline as Code is apractical guide to automating your development pipeline in a cloud-native, service-driven world. YouGÇÖll use the latest infrastructure-as-code tools likePacker and Terraform to develop reliable CI/CD pipelines for numerous cloud-native applications. Follow this book's insightful best practices, and youGÇÖll soon be delivering software thatGÇÖs quicker to market, faster to deploy,and with less last-minute production bugs.

Learn PowerShell in a Month of Lunches covers Windows, Linux, and macOS is a task-focused tutorial for administering Linuxand macOS systems using Microsoft PowerShell. Adapted by PowerShell team members Travis Plunk and Tyler Leonhardt from the best selling Learn Windows PowerShell in a Month of Lunches by community legends DonJones and Jeffrey Hicks, it features Linux-based examples covering core language features and admin tasks. Designed for busy IT professionals, this innovative guide will take you from the basics to PowerShell proficiency through 25 tutorials you can do in your lunch break

No-nonsense techniques, libraries, and best practices you can use to keep your Python applications safe and secure.In Full Stack Python Security: Cryptography, TLS, and attack resistance, youll learn how to:Use algorithms to encrypt, hash, and digitally sign dataCreate and install TLS certificatesImplement authentication, authorization, OAuth 2.0, and form validation in DjangoProtect a web application with Content Security PolicyImplement Cross Origin Resource SharingProtect against common attacks including clickjacking, denial of service attacks, SQL injection, cross-site scripting, and moreFull Stack Python Security: Cryptography, TLS, and attack resistance teaches you everything youll need to build secure Python web applications. As you work through the insightful code snippets and engaging examples, youll put security standards, best practices, and more into action. Along the way, youll get exposure to important libraries and tools in the Python ecosystem.

Design, develop, and deploy human-like AI solutions that chat with your customers, solve their problems, and streamline your support services.In Conversational AI, you will learn how to:Pick the right AI assistant type and channel for your needsWrite dialog with intentional tone and specificityTrain your AIs classifier from the ground upCreate question-and-direct-response AI assistantsDesign and optimize a process flow for web and voiceTest your assistants accuracy and plan out improvementsConversational AI: Chatbots that work teaches you to create the kind of AI-enabled assistants that are revolutionizing the customer service industry. Youll learn to build effective conversational AI that can automate common inquiries and easily address your customers' most common problems. This engaging and entertaining book delivers the essential technical and creative skills for designing successful AI solutions, from coding process flows and training machine learning, to improving your written dialog.

Build fast, efficient Kubernetes-based Java applications using the Quarkus framework, MicroProfile, and Java standards.Most popular Java frameworks, like Spring, were designed long before the advent of Kubernetes and cloud-native systems. A new generation of tools, including Quarkus and MicroProfile have been cloud-native and Kubernetes-aware from the beginning. Kubernetes Native Microservices: With Quarkus and MicroProfile teaches you how to create efficient enterprise Java applications that are easy to deploy, maintain, and expand.In Kubernetes Native Microservices: With Quarkus and MicroProfile youll learn how to:Deploy enterprise Java applications on KubernetesDevelop applications using the Quarkus runtime frameworkCompile natively using GraalVM for blazing speedCreate efficient microservices applicationsTake advantage of MicroProfile specifications

Logging in Action teaches you how to make your log processing a real asset for your application, all with free and open source tools. YouGÇÖll use the powerful log management tool Fluentd to solve common log problems, and learn how proper log management can improve performance and make management of software solutions easier. Through useful examples like sending log driven events to Slack, youGÇÖll get hands-on experience applying structure to your unstructured data.

Grokking Functional Programming is a practical book written especially for object-oriented programmers. It will help you map familiar ideas like objects and composition to FP concepts such as programming with immutable data and higher-order functions. You will learn how to write concurrent programs, how to handle errors and how to design your solutions with modularity and readability in mind. And you'll be pleased to know that we skip the academic baggage of lambda calculus, category theory, and the mathematical foundations of FP in favour of applying functional programming to everyday programming tasks. At the end of the book, you'll be ready to pick a functional language and start writing useful and maintainable software.

Own Your Tech Career: Soft skills for technologists is a guide to taking control of your professional life. It teaches you to approach your career with planning and purpose, always making active decisions towards your goals.Summary In Own Your Tech Career: Soft skills for technologists, you will:     Define what “success” means for your career     Discover personal branding and career maintenance     Prepare for and conduct a tech job hunt     Spot speed bumps and barriers that can derail your progress     Learn how to navigate the rules of the business world     Perform market analysis to keep your tech skills fresh and relevant Whatever your road to success, you’ll benefit from the toolbox of career-boosting techniques you’ll find in Own Your Tech Career: Soft skills for technologists. You’ll discover in-demand communication and teamwork skills, essential rules for professionalism, tactics of the modern job hunt, and more. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A successful technology career demands more than just technical ability. Achieving your goals requires clear communication, top-notch time management, and a knack for navigating business needs. Master the “soft skills,” and you’ll have a smoother path to success and satisfaction, however you define that for yourself. About the book Own Your Tech Career: Soft skills for technologists helps you get what you want out of your technology career. You’ll start by defining your ambition—whether that’s a salary, a job title, a flexible schedule, or something else. Once you know where you’re going, this book’s adaptable advice guides your journey. You’ll learn conflict resolution and teamwork, master nine rules of professionalism, and build the confidence and skill you need to stay on the path you’ve set for yourself. What's inside     Personal branding and career maintenance     Barriers that derail progress     The rules of the business world     Market analysis to keep tech skills fresh About the reader For tech professionals who want to take control of their career. About the author Microsoft MVP Don Jones brings his years of experience as a successful IT trainer to this engaging guide. Table of Contents 1 Own your career 2 Build and maintain your brand 3 Network 4 Be part of a technology community 5 Keep your tech skills fresh and relevant 6 Show up as a professional 7 Manage your time 8 Handle remote work 9 Be a team player 10 Be a team leade 11 Solve problems 12 Conquer written communications 13 Conquer verbal communications 14 Resolve conflicts 15 Be a data-driven, critical thinker 16 Understand how businesses work 17 Be a better decision-maker 18 Help others 19 Be prepared for anything 20 Business math and terminology for technologists 21 Tools for the modern job hunt

Microservices in .NET, Second Edition teaches you to build and deploy microservices using ASP.NET and Azure services.Summary In Microservices in .NET, Second Edition you will learn how to:     Build scalable microservices that are reliable in production     Optimize microservices for continuous delivery     Design event-based collaboration between microservices     Deploy microservices to Kubernetes     Set up Kubernetes in Azure Microservices in .NET, Second Edition is a comprehensive guide to building microservice applications using the .NET stack. After a crystal-clear introduction to the microservices architectural style, it teaches you practical microservices development skills using ASP.NET. This second edition of the bestselling original has been revised with up-to-date tools for the .NET ecosystem, and more new coverage of scoping microservices and deploying to Kubernetes. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Microservice architectures connect independent components that must work together as a system. Integrating new technologies like Docker and Kubernetes with Microsoft’s familiar ASP.NET framework and Azure cloud platform enables .NET developers to create and manage microservices efficiently. About the book Microservices in .NET, Second Edition teaches you to build and deploy microservices using ASP.NET and Azure services. It lays out microservice architecture simply, and then guides you through several real-world projects, such as building an ecommerce shopping cart. In this fully revised edition, you’ll learn about scoping microservices, deploying to Kubernetes, and operations concerns like monitoring, logging, and security. What's inside     Optimize microservices for continuous delivery     Design event-based collaboration between microservices     Deploy microservices to Kubernetes     Set up Kubernetes in Azure About the reader For C# developers. No experience with microservices required. About the author Christian Horsdal is an independent consultant with more than 20 years of experience building projects from large-scale microservice systems to tiny embedded systems. Table of Contents PART 1 GETTING STARTED WITH MICROSERVICES 1 Microservices at a glance 2 A basic shopping cart microservice 3 Deploying a microservice to Kubernetes PART 2 BUILDING MICROSERVICES 4 Identifying and scoping microservices 5 Microservice collaboration 6 Data ownership and data storage 7 Designing for robustness 8 Writing tests for microservices PART 3 HANDLING CROSS-CUTTING CONCERNS: BUILDING A REUSABLE MICROSERVICE PLATFORM 9 Cross-cutting concerns: Monitoring and logging 10 Securing microservice-to-microservice communication 11 Building a reusable microservice platform PART 4 BUILDING APPLICATIONS 12 Creating applications over microservices

Take the next steps in your data science career! This friendly and hands-on guide shows you how to start mastering Pandas with skills you already know from spreadsheet software.In Pandas in Action you will learn how to:     Import datasets, identify issues with their data structures, and optimize them for efficiency     Sort, filter, pivot, and draw conclusions from a dataset and its subsets     Identify trends from text-based and time-based data     Organize, group, merge, and join separate datasets     Use a GroupBy object to store multiple DataFrames Pandas has rapidly become one of Python's most popular data analysis libraries. In Pandas in Action, a friendly and example-rich introduction, author Boris Paskhaver shows you how to master this versatile tool and take the next steps in your data science career. You’ll learn how easy Pandas makes it to efficiently sort, analyze, filter and munge almost any type of data. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Data analysis with Python doesn’t have to be hard. If you can use a spreadsheet, you can learn pandas! While its grid-style layouts may remind you of Excel, pandas is far more flexible and powerful. This Python library quickly performs operations on millions of rows, and it interfaces easily with other tools in the Python data ecosystem. It’s a perfect way to up your data game. About the book Pandas in Action introduces Python-based data analysis using the amazing pandas library. You’ll learn to automate repetitive operations and gain deeper insights into your data that would be impractical—or impossible—in Excel. Each chapter is a self-contained tutorial. Realistic downloadable datasets help you learn from the kind of messy data you’ll find in the real world. What's inside     Organize, group, merge, split, and join datasets     Find trends in text-based and time-based data     Sort, filter, pivot, optimize, and draw conclusions     Apply aggregate operations About the reader For readers experienced with spreadsheets and basic Python programming. About the author Boris Paskhaver is a software engineer, Agile consultant, and online educator. His programming courses have been taken by 300,000 students across 190 countries. Table of Contents PART 1 CORE PANDAS 1 Introducing pandas 2 The Series object 3 Series methods 4 The DataFrame object 5 Filtering a DataFrame PART 2 APPLIED PANDAS 6 Working with text data 7 MultiIndex DataFrames 8 Reshaping and pivoting 9 The GroupBy object 10 Merging, joining, and concatenating 11 Working with dates and times 12 Imports and exports 13 Configuring pandas 14 Visualization

Spring Microservices in Action, Second Edition teaches you to build microservice-based applications using Java and Spring.Summary By dividing large applications into separate self-contained units, Microservices are a great step toward reducing complexity and increasing flexibility. Spring Microservices in Action, Second Edition teaches you how to build microservice-based applications using Java and the Spring platform. This second edition is fully updated for the latest version of Spring, with expanded coverage of API routing with Spring Cloud Gateway, logging with the ELK stack, metrics with Prometheus and Grafana, security with the Hashicorp Vault, and modern deployment practices with Kubernetes and Istio. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Building and deploying microservices can be easy in Spring! Libraries like Spring Boot, Spring Cloud, and Spring Cloud Gateway reduce the boilerplate code in REST-based services. They provide an effective toolbox to get your microservices up and running on both public and private clouds. About the book Spring Microservices in Action, Second Edition teaches you to build microservice-based applications using Java and Spring. You’ll start by creating basic services, then move to efficient logging and monitoring. Learn to refactor Java applications with Spring’s intuitive tooling, and master API management with Spring Cloud Gateway. You’ll even deploy Spring Cloud applications with AWS and Kubernetes. What's inside     Microservice design principles and best practices     Configuration with Spring Cloud Config and Hashicorp Vault     Client-side resiliency with Resilience4j, and Spring Cloud Load Balancer     Metrics monitoring with Prometheus and Grafana     Distributed tracing with Spring Cloud Sleuth, Zipkin, and ELK Stack About the reader For experienced Java and Spring developers. About the author John Carnell is a senior cloud engineer with 20 years of Java experience. Illary Huaylupo Sánchez is a software engineer with over 13 years of experience. Table of Contents 1 Welcome to the cloud, Spring 2 Exploring the microservices world with Spring Cloud 3 Building microservices with Spring Boot 4 Welcome to Docker 5 Controlling your configuration with the Spring Cloud Configuration Server 6 On service discovery 7 When bad things happen: Resiliency patterns with Spring Cloud and Resilience4j 8 Service routing with Spring Cloud Gateway 9 Securing your microservices 10 Event-driven architecture with Spring Cloud Stream 11 Distributed tracing with Spring Cloud Sleuth and Zipkin 12 Deploying your microservices

Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing.Summary As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Can you improve the speed and efficiency of your applications without investing in new hardware? Well, yes, you can: Innovations in algorithms and data structures have led to huge advances in application performance. Pick up this book to discover a collection of advanced algorithms that will make you a more effective developer. About the book Advanced Algorithms and Data Structures introduces a collection of algorithms for complex programming challenges in data analysis, machine learning, and graph computing. You’ll discover cutting-edge approaches to a variety of tricky scenarios. You’ll even learn to design your own data structures for projects that require a custom solution. What's inside     Build on basic data structures you already know     Profile your algorithms to speed up application     Store and query strings efficiently     Distribute clustering algorithms with MapReduce     Solve logistics problems using graphs and optimization algorithms About the reader For intermediate programmers. About the author Marcello La Rocca is a research scientist and a full-stack engineer. His focus is on optimization algorithms, genetic algorithms, machine learning, and quantum computing. Table of Contents 1 Introducing data structures PART 1 IMPROVING OVER BASIC DATA STRUCTURES 2 Improving priority queues: d-way heaps 3 Treaps: Using randomization to balance binary search trees 4 Bloom filters: Reducing the memory for tracking content 5 Disjoint sets: Sub-linear time processing 6 Trie, radix trie: Efficient string search 7 Use case: LRU cache PART 2 MULTIDEMENSIONAL QUERIES 8 Nearest neighbors search 9 K-d trees: Multidimensional data indexing 10 Similarity Search Trees: Approximate nearest neighbors search for image retrieval 11 Applications of nearest neighbor search 12 Clustering 13 Parallel clustering: MapReduce and canopy clustering PART 3 PLANAR GRAPHS AND MINIMUM CROSSING NUMBER 14 An introduction to graphs: Finding paths of minimum distance 15 Graph embeddings and planarity: Drawing graphs with minimal edge intersections 16 Gradient descent: Optimization problems (not just) on graphs 17 Simulated annealing: Optimization beyond local minima 18 Genetic algorithms: Biologically inspired, fast-converging optimization

Five Lines of Code teaches refactoring that's focused on concrete rules and getting any method down to five lines or less! There’s no jargon or tricky automated-testing skills required, just easy guidelines and patterns illustrated by detailed code samples.In Five Lines of Code you will learn:     The signs of bad code     Improving code safely, even when you don’t understand it     Balancing optimization and code generality     Proper compiler practices     The Extract method, Introducing Strategy pattern, and many other refactoring patterns     Writing stable code that enables change-by-addition     Writing code that needs no comments     Real-world practices for great refactoring Improving existing code—refactoring—is one of the most common tasks you’ll face as a programmer. Five Lines of Code teaches you clear and actionable refactoring rules that you can apply without relying on intuitive judgements such as “code smells.” Following the author’s expert perspective—that refactoring and code smells can be learned by following a concrete set of principles—you’ll learn when to refactor your code, what patterns to apply to what problem, and the code characteristics that indicate it’s time for a rework. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Every codebase includes mistakes and inefficiencies that you need to find and fix. Refactor the right way, and your code becomes elegant, easy to read, and easy to maintain. In this book, you’ll learn a unique approach to refactoring that implements any method in five lines or fewer. You’ll also discover a secret most senior devs know: sometimes it’s quicker to hammer out code and fix it later! About the book Five Lines of Code is a fresh look at refactoring for developers of all skill levels. In it, you’ll master author Christian Clausen’s innovative approach, learning concrete rules to get any method down to five lines—or less! You’ll learn when to refactor, specific refactoring patterns that apply to most common problems, and characteristics of code that should be deleted altogether. What's inside     The signs of bad code     Improving code safely, even when you don’t understand it     Balancing optimization and code generality     Proper compiler practices About the reader For developers of all skill levels. Examples use easy-to-read Typescript, in the same style as Java and C#. About the author Christian Clausen works as a Technical Agile Coach, teaching teams how to refactor code. Table of Contents 1 Refactoring refactoring 2 Looking under the hood of refactoring PART 1 LEARN BY REFACTORING A COMPUTER GAME 3 Shatter long function 4 Make type codes work 5 Fuse similar code together 6 Defend the data PART 2 TAKING WHAT YOU HAVE LEARNED INTO THE REAL WORLD 7 Collaborate with the compiler 8 Stay away from comments 9 Love deleting code 10 Never be afraid to add code 11 Follow the structure in the code 12 Avoid optimizations and generality 13 Make bad code look bad 14 Wrapping up

Discover best practices, reproducible architectures, and design patterns to help guide deep learning models from the lab into production.In Deep Learning Patterns and Practices you will learn:     Internal functioning of modern convolutional neural networks     Procedural reuse design pattern for CNN architectures     Models for mobile and IoT devices     Assembling large-scale model deployments     Optimizing hyperparameter tuning     Migrating a model to a production environment The big challenge of deep learning lies in taking cutting-edge technologies from R&D labs through to production. Deep Learning Patterns and Practices is here to help. This unique guide lays out the latest deep learning insights from author Andrew Ferlitsch’s work with Google Cloud AI. In it, you'll find deep learning models presented in a unique new way: as extendable design patterns you can easily plug-and-play into your software projects. Each valuable technique is presented in a way that's easy to understand and filled with accessible diagrams and code samples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Discover best practices, design patterns, and reproducible architectures that will guide your deep learning projects from the lab into production. This awesome book collects and illuminates the most relevant insights from a decade of real world deep learning experience. You’ll build your skills and confidence with each interesting example. About the book Deep Learning Patterns and Practices is a deep dive into building successful deep learning applications. You’ll save hours of trial-and-error by applying proven patterns and practices to your own projects. Tested code samples, real-world examples, and a brilliant narrative style make even complex concepts simple and engaging. Along the way, you’ll get tips for deploying, testing, and maintaining your projects. What's inside     Modern convolutional neural networks     Design pattern for CNN architectures     Models for mobile and IoT devices     Large-scale model deployments     Examples for computer vision About the reader For machine learning engineers familiar with Python and deep learning. About the author Andrew Ferlitsch is an expert on computer vision, deep learning, and operationalizing ML in production at Google Cloud AI Developer Relations. Table of Contents PART 1 DEEP LEARNING FUNDAMENTALS 1 Designing modern machine learning 2 Deep neural networks 3 Convolutional and residual neural networks 4 Training fundamentals PART 2 BASIC DESIGN PATTERN 5 Procedural design pattern 6 Wide convolutional neural networks 7 Alternative connectivity patterns 8 Mobile convolutional neural networks 9 Autoencoders PART 3 WORKING WITH PIPELINES 10 Hyperparameter tuning 11 Transfer learning 12 Data distributions 13 Data pipeline 14 Training and deployment pipeline

Build hyper-fast and hyper-efficient web applications with GraphQL! This practical, comprehensive guide introduces the powerful GRANDStack for developing full stack web applications based in graph data structures.In Full Stack GraphQL Applications you will learn how to:     Build backend functionalities for GraphQL applications     Model a GraphQL API with GraphQL type definitions     Utilize Neo4j as a backend database     Handle authentication and authorization with GraphQL     Implement pagination and rate limiting in a GraphQL API     Develop a GraphQL service with Apollo Server     Install Neo4j Database on different platforms     Create a basic frontend application using React and Apollo Client     Deploy a full stack GraphQL application to the cloud The GraphQL query language radically reduces over-fetching or under-fetching of data by constructing precise graph-based data requests. In Full Stack GraphQL Applications you’ll learn how to build graph-aware web applications that take full advantage of GraphQL’s amazing efficiency. Neo4j’s William Lyon teaches you everything you need to know to design, deploy, and maintain a GraphQL API from scratch. He reveals how you can build your web apps with GraphQL, React, Apollo, and Neo4j Database, aka “the GRANDstack,” to get maximum performance out of GraphQL. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology The GraphQL API query language radically streamlines data exchanges with backend servers by representing application data as easy-to-understand graphs. You can amplify GraphQL’s benefits by using graph-aware tools and data stores, like React, Apollo, and Neo4j, throughout your application. A full stack graph approach provides a consistent data model end to end, reducing friction in data fetching and increasing developer productivity. About the book Full Stack GraphQL Applications teaches you to build graph-aware web applications using GraphQL, React, Apollo, and the Neo4j database, collectively called “the GRANDstack.” Practical, hands-on examples quickly develop your understanding of how the GRANDstack fits together. As you go, you’ll create and deploy to the cloud a full-featured web application that includes search, authentication, and more. Soon, you’ll be ready to deploy end-to-end applications that take full advantage of GraphQL’s outstanding performance. What's inside     Building a GraphQL backend using Neo4j     Authentication and authorization with GraphQL     Pagination and GraphQL abstract types     A basic frontend application using React and Apollo Client     Deploying to the cloud with Netlify, AWS Lambda, Auth0, and Neo4j Aura About the reader For full stack web developers. No experience with GraphQL or graph databases required. About the author William Lyon is a Staff Developer Advocate at Neo4j and blogger at lyonwj.com. Table of Contents PART 1 GETTING STARTED WITH FULL STACK GRAPHQL 1 What is full stack GraphQL? 2 Graph thinking with GraphQL 3 Graphs in the database 4 The Neo4j GraphQL Library PART 2 BUILDING THE FRONTEND 5 Building user interfaces with React 6 Client-side GraphQL with React and Apollo Client PART 3 FULL STACK CONSIDERATIONS 7 Adding authorization and authentication 8 Deploying our full stack GraphQL application 9 Advanced GraphQL considerations

Build custom NLP models in record time by adapting pre-trained machine learning models to solve specialized problems.Summary In Transfer Learning for Natural Language Processing you will learn:     Fine tuning pretrained models with new domain data     Picking the right model to reduce resource usage     Transfer learning for neural network architectures     Generating text with generative pretrained transformers     Cross-lingual transfer learning with BERT     Foundations for exploring NLP academic literature Training deep learning NLP models from scratch is costly, time-consuming, and requires massive amounts of data. In Transfer Learning for Natural Language Processing, DARPA researcher Paul Azunre reveals cutting-edge transfer learning techniques that apply customizable pretrained models to your own NLP architectures. You’ll learn how to use transfer learning to deliver state-of-the-art results for language comprehension, even when working with limited label data. Best of all, you’ll save on training time and computational costs. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build custom NLP models in record time, even with limited datasets! Transfer learning is a machine learning technique for adapting pretrained machine learning models to solve specialized problems. This powerful approach has revolutionized natural language processing, driving improvements in machine translation, business analytics, and natural language generation. About the book Transfer Learning for Natural Language Processing teaches you to create powerful NLP solutions quickly by building on existing pretrained models. This instantly useful book provides crystal-clear explanations of the concepts you need to grok transfer learning along with hands-on examples so you can practice your new skills immediately. As you go, you’ll apply state-of-the-art transfer learning methods to create a spam email classifier, a fact checker, and more real-world applications. What's inside     Fine tuning pretrained models with new domain data     Picking the right model to reduce resource use     Transfer learning for neural network architectures     Generating text with pretrained transformers About the reader For machine learning engineers and data scientists with some experience in NLP. About the author Paul Azunre holds a PhD in Computer Science from MIT and has served as a Principal Investigator on several DARPA research programs. Table of Contents PART 1 INTRODUCTION AND OVERVIEW 1 What is transfer learning? 2 Getting started with baselines: Data preprocessing 3 Getting started with baselines: Benchmarking and optimization PART 2 SHALLOW TRANSFER LEARNING AND DEEP TRANSFER LEARNING WITH RECURRENT NEURAL NETWORKS (RNNS) 4 Shallow transfer learning for NLP 5 Preprocessing data for recurrent neural network deep transfer learning experiments 6 Deep transfer learning for NLP with recurrent neural networks PART 3 DEEP TRANSFER LEARNING WITH TRANSFORMERS AND ADAPTATION STRATEGIES 7 Deep transfer learning for NLP with the transformer and GPT 8 Deep transfer learning for NLP with BERT and multilingual BERT 9 ULMFiT and knowledge distillation adaptation strategies 10 ALBERT, adapters, and multitask adaptation strategies 11 Conclusions

Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness.Summary Complex calculations, like training deep learning models or running large-scale simulations, can take an extremely long time. Efficient parallel programming can save hours—or even days—of computing time. Parallel and High Performance Computing shows you how to deliver faster run-times, greater scalability, and increased energy efficiency to your programs by mastering parallel techniques for multicore processor and GPU hardware. About the technology Write fast, powerful, energy efficient programs that scale to tackle huge volumes of data. Using parallel programming, your code spreads data processing tasks across multiple CPUs for radically better performance. With a little help, you can create software that maximizes both speed and efficiency. About the book Parallel and High Performance Computing offers techniques guaranteed to boost your code’s effectiveness. You’ll learn to evaluate hardware architectures and work with industry standard tools such as OpenMP and MPI. You’ll master the data structures and algorithms best suited for high performance computing and learn techniques that save energy on handheld devices. You’ll even run a massive tsunami simulation across a bank of GPUs. What's inside     Planning a new parallel project     Understanding differences in CPU and GPU architecture     Addressing underperforming kernels and loops     Managing applications with batch scheduling About the reader For experienced programmers proficient with a high-performance computing language like C, C++, or Fortran. About the author Robert Robey works at Los Alamos National Laboratory and has been active in the field of parallel computing for over 30 years. Yuliana Zamora is currently a PhD student and Siebel Scholar at the University of Chicago, and has lectured on programming modern hardware at numerous national conferences. Table of Contents PART 1 INTRODUCTION TO PARALLEL COMPUTING 1 Why parallel computing? 2 Planning for parallelization 3 Performance limits and profiling 4 Data design and performance models 5 Parallel algorithms and patterns PART 2 CPU: THE PARALLEL WORKHORSE 6 Vectorization: FLOPs for free 7 OpenMP that performs 8 MPI: The parallel backbone PART 3 GPUS: BUILT TO ACCELERATE 9 GPU architectures and concepts 10 GPU programming model 11 Directive-based GPU programming 12 GPU languages: Getting down to basics 13 GPU profiling and tools PART 4 HIGH PERFORMANCE COMPUTING ECOSYSTEMS 14 Affinity: Truce with the kernel 15 Batch schedulers: Bringing order to chaos 16 File operations for a parallel world 17 Tools and resources for better code

Knative in Action teaches you to build complex and efficient serverless applications.Summary Take the pain out of managing serverless applications. Knative, a collection of Kubernetes extensions curated by Google, simplifies building and running serverless systems. Knative in Action guides you through the Knative toolkit, showing you how to launch, modify, and monitor event-based apps built using cloud-hosted functions like AWS Lambda. You’ll learn how to use Knative Serving to develop software that is easily deployed and autoscaled, how to use Knative Eventing to wire together disparate systems into a consistent whole, and how to integrate Knative into your shipping pipeline. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology With Knative, managing a serverless application’s full lifecycle is a snap. Knative builds on Kubernetes orchestration features, making it easy to deploy and run serverless apps. It handles low-level chores—such as starting and stopping instances—so you can concentrate on features and behavior. About the book Knative in Action teaches you to build complex and efficient serverless applications. You’ll dive into Knative’s unique design principles and grasp cloud native concepts like handling latency-sensitive workloads. You’ll deliver updates with Knative Serving and interlink apps, services, and systems with Knative Eventing. To keep you moving forward, every example includes deployment advice and tips for debugging. What's inside     Deploy a service with Knative Serving     Connect systems with Knative Eventing     Autoscale responses for different traffic surges     Develop, ship, and operate software About the reader For software developers comfortable with CLI tools and an OO language like Java or Go. About the author Jacques Chester has worked in Pivotal and VMWare R&D since 2014, contributing to Knative and other projects. Table of Contents 1 Introduction 2 Introducing Knative Serving 3 Configurations and Revisions 4 Routes 5 Autoscaling 6 Introduction to Eventing 7 Sources and Sinks 8 Filtering and Flowing 9 From Conception to Production

In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors.Summary Centralized data warehouses, the long-time defacto standard for housing data for analytics, are rapidly giving way to multi-faceted cloud data platforms. Companies that embrace modern cloud data platforms benefit from an integrated view of their business using all of their data and can take advantage of advanced analytic practices to drive predictions and as yet unimagined data services. Designing Cloud Data Platforms is a hands-on guide to envisioning and designing a modern scalable data platform that takes full advantage of the flexibility of the cloud. As you read, you’ll learn the core components of a cloud data platform design, along with the role of key technologies like Spark and Kafka Streams. You’ll also explore setting up processes to manage cloud-based data, keep it secure, and using advanced analytic and BI tools to analyze it. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Well-designed pipelines, storage systems, and APIs eliminate the complicated scaling and maintenance required with on-prem data centers. Once you learn the patterns for designing cloud data platforms, you’ll maximize performance no matter which cloud vendor you use. About the book In Designing Cloud Data Platforms, Danil Zburivsky and Lynda Partner reveal a six-layer approach that increases flexibility and reduces costs. Discover patterns for ingesting data from a variety of sources, then learn to harness pre-built services provided by cloud vendors. What's inside     Best practices for structured and unstructured data sets     Cloud-ready machine learning tools     Metadata and real-time analytics     Defensive architecture, access, and security About the reader For data professionals familiar with the basics of cloud computing, and Hadoop or Spark. About the author Danil Zburivsky has over 10 years of experience designing and supporting large-scale data infrastructure for enterprises across the globe. Lynda Partner is the VP of Analytics-as-a-Service at Pythian, and has been on the business side of data for over 20 years. Table of Contents 1 Introducing the data platform 2 Why a data platform and not just a data warehouse 3 Getting bigger and leveraging the Big 3: Amazon, Microsoft Azure, and Google 4 Getting data into the platform 5 Organizing and processing data 6 Real-time data processing and analytics 7 Metadata layer architecture 8 Schema management 9 Data access and security 10 Fueling business value with data platforms

Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together.In Real-world Natural Language Processing you will learn how to:     Design, develop, and deploy useful NLP applications     Create named entity taggers     Build machine translation systems     Construct language generation systems and chatbots     Use advanced NLP concepts such as attention and transfer learning Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In this engaging book, you’ll explore the core tools and techniques required to build a huge range of powerful NLP apps, including chatbots, language detectors, and text classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Training computers to interpret and generate speech and text is a monumental challenge, and the payoff for reducing labor and improving human/computer interaction is huge! Th e field of Natural Language Processing (NLP) is advancing rapidly, with countless new tools and practices. This unique book offers an innovative collection of NLP techniques with applications in machine translation, voice assistants, text generation, and more. About the book Real-world Natural Language Processing shows you how to build the practical NLP applications that are transforming the way humans and computers work together. Guided by clear explanations of each core NLP topic, you’ll create many interesting applications including a sentiment analyzer and a chatbot. Along the way, you’ll use Python and open source libraries like AllenNLP and HuggingFace Transformers to speed up your development process. What's inside     Design, develop, and deploy useful NLP applications     Create named entity taggers     Build machine translation systems     Construct language generation systems and chatbots About the reader For Python programmers. No prior machine learning knowledge assumed. About the author Masato Hagiwara received his computer science PhD from Nagoya University in 2009. He has interned at Google and Microsoft Research, and worked at Duolingo as a Senior Machine Learning Engineer. He now runs his own research and consulting company. Table of Contents PART 1 BASICS 1 Introduction to natural language processing 2 Your first NLP application 3 Word and document embeddings 4 Sentence classification 5 Sequential labeling and language modeling PART 2 ADVANCED MODELS 6 Sequence-to-sequence models 7 Convolutional neural networks 8 Attention and Transformer 9 Transfer learning with pretrained language models PART 3 PUTTING INTO PRODUCTION 10 Best practices in developing NLP applications 11 Deploying and serving NLP applications

Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science.In Data Science Bookcamp you will learn:     Techniques for computing and plotting probabilities     Statistical analysis using Scipy     How to organize datasets with clustering algorithms     How to visualize complex multi-variable datasets     How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside     Web scraping     Organize datasets with clustering algorithms     Visualize complex multi-variable datasets     Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution

The perfect starting point for your journey into Scala and functional programming.Summary In Get Programming in Scala you will learn:     Object-oriented principles in Scala     Express program designs in functions     Use types to enforce program requirements     Use abstractions to avoid code duplication     Write meaningful tests and recognize code smells Scala is a multi-style programming language for the JVM that supports both object-oriented and functional programming. Master Scala, and you'll be well-equipped to match your programming approach to the type of problem you're dealing with. Packed with examples and exercises, Get Programming with Scala is the perfect starting point for developers with some OO knowledge who want to learn Scala and pick up a few FP skills along the way. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Scala developers are in high demand. This flexible language blends object-oriented and functional programming styles so you can write flexible, easy-to-maintain code. Because Scala runs on the JVM, your programs can interact seamlessly with Java libraries and tools. If you’re comfortable writing Java, this easy-to-read book will get you programming with Scala fast. About the book Get Programming with Scala is a fast-paced introduction to the Scala language, covering both Scala 2 and Scala 3. You’ll learn through lessons, quizzes, and hands-on projects that bring your new skills to life. Clear explanations make Scala’s features and abstractions easy to understand. As you go, you’ll learn to write familiar object-oriented code in Scala and also discover the possibilities of functional programming. What's inside     Apply object-oriented principles in Scala     Learn the core concepts of functional programming     Use types to enforce program requirements     Use abstractions to avoid code duplication     Write meaningful tests and recognize code smells About the reader For developers who know an OOP language like Java, Python, or C#. No experience with Scala or functional programming required. About the author Daniela Sfregola is a Senior Software Engineer and a Scala user since 2013. She is an active contributor to the Scala Community, a public speaker at Scala conferences and meetups, and a maintainer of open-source projects. Table of Contents Unit 0 HELLO SCALA! Unit 1 THE BASICS Unit 2 OBJECT-ORIENTED FUNDAMENTALS Unit 3 HTTP SERVER Unit 4 IMMUTABLE DATA AND STRUCTURES Unit 5 LIST Unit 6 OTHER COLLECTIONS AND ERROR HANDLING Unit 7 CONCURRENCY Unit 8 JSON (DE)SERIALIZATION

If you're browsing the web, using public APIs, making and receiving electronic payments, registering and logging in users, or experimenting with blockchain, you're relying on cryptography. And you're probably trusting a collection of tools, frameworks, and protocols to keep your data, users, and business safe. It's important to understand these tools so you can make the best decisions about how, where, and why to use them. Real-World Cryptography teaches you applied cryptographic techniques to understand and apply security at every level of your systems and applications.about the technologyCryptography is the foundation of information security. This simultaneously ancient and emerging science is based on encryption and secure communication using algorithms that are hard to crack even for high-powered computer systems. Cryptography protects privacy, secures online activity, and defends confidential information, such as credit cards, from attackers and thieves. Without cryptographic techniques allowing for easy encrypting and decrypting of data, almost all IT infrastructure would be vulnerable.about the bookReal-World Cryptography helps you understand the cryptographic techniques at work in common tools, frameworks, and protocols so you can make excellent security choices for your systems and applications. There's no unnecessary theory or jargon-just the most up-to-date techniques you'll need in your day-to-day work as a developer or systems administrator. Cryptography expert David Wong takes you hands-on with cryptography building blocks such as hash functions and key exchanges, then shows you how to use them as part of your security protocols and applications. Alongside modern methods, the book also anticipates the future of cryptography, diving into emerging and cutting-edge advances such as cryptocurrencies, password-authenticated key exchange, and post-quantum cryptography. Throughout, all techniques are fully illustrated with diagrams and real-world use cases so you can easily see how to put them into practice. what's insideBest practices for using cryptographyDiagrams and explanations of cryptographic algorithmsIdentifying and fixing cryptography bad practices in applicationsPicking the right cryptographic tool to solve problemsabout the readerFor cryptography beginners with no previous experience in the field.about the authorDavid Wong is a senior engineer working on Blockchain at Facebook. He is an active contributor to internet standards like Transport Layer Security and to the applied cryptography research community. David is a recognized authority in the field of applied cryptography; he's spoken at large security conferences like Black Hat and DEF CON and has delivered cryptography training sessions in the industry.

Functional Programming in Kotlin is a reworked version of the bestselling Functional Programming in Scala, with all code samples, instructions, and exercises translated into the powerful Kotlin language. In this authoritative guide, you'll take on the challenge of learning functional programming from first principles, and start writing Kotlin code that's easier to read, easier to reuse, better for concurrency, and less prone to bugs and errors.about the technologyKotlin is a new JVM language designed to interoperate with Java and offer an improved developer experience for creating new applications. It's already a top choice for writing web services, and Android apps. Although it preserves Java's OO roots, Kotlin really shines when you adopt a functional programming mindset. By learning the core principles and practices of functional programming outlined in this book, you'll start writing code that's easier to read, easier to test and reuse, better for concurrency, and less prone to bugs.about the bookFunctional Programming in Kotlin is a serious tutorial for programmers looking to learn FP and apply it to the everyday business of coding. Based on the bestselling Functional Programming in Scala, this book guides intermediate Java and Kotlin programmers from basic techniques to advanced topics in a logical, concise, and clear progression. In it, you'll find concrete examples and exercises that open up the world of functional programming. The book will deliver practical mastery of FP using Kotlin and a valuable perspective on program design that you can apply to other languages. what's insideFunctional programming techniques for real-world applicationsWrite combinator librariesIdentify common structures and idioms in functional designCode for simplicity, modularity, and fewer bugsabout the readerFor intermediate Kotlin and Java developers. No experience with functional programming is required.about the authorMarco Vermeulen has almost two decades of programming experience on the JVM, with much of that time spent on functional programming using Scala and Kotlin.Rúnar Bjarnason and Paul Chiusano are the authors of Functional Programming in Scala, on which this book is based. They are internationally-recognized experts in functional programming and the Scala programming language.

Deploying a machine learning model into a fully realized production system usually requires painstaking work by an operations team creating and managing custom servers. Cloud Native Machine Learning helps you bridge that gap by using the pre-built services provided by cloud platforms like Azure and AWS to assemble your ML system's infrastructure. Following a real-world use case for calculating taxi fares, you'll learn how to get a serverless ML pipeline up and running using AWS services. Clear and detailed tutorials show you how to develop reliable, flexible, and scalable machine learning systems without time-consuming management tasks or the costly overheads of physical hardware.about the technologyYour new machine learning model is ready to put into production, and suddenly all your time is taken up by setting up your server infrastructure. Serverless machine learning offers a productivity-boosting alternative. It eliminates the time-consuming operations tasks from your machine learning lifecycle, letting out-of-the-box cloud services take over launching, running, and managing your ML systems. With the serverless capabilities of major cloud vendors handling your infrastructure, you're free to focus on tuning and improving your models.about the bookCloud Native Machine Learning is a guide to bringing your experimental machine learning code to production using serverless capabilities from major cloud providers. You'll start with best practices for your datasets, learning to bring VACUUM data-quality principles to your projects, and ensure that your datasets can be reproducibly sampled. Next, you'll learn to implement machine learning models with PyTorch, discovering how to scale up your models in the cloud and how to use PyTorch Lightning for distributed ML training. Finally, you'll tune and engineer your serverless machine learning pipeline for scalability, elasticity, and ease of monitoring with the built-in notification tools of your cloud platform. When you're done, you'll have the tools to easily bridge the gap between ML models and a fully functioning production system. what's insideExtracting, transforming, and loading datasetsQuerying datasets with SQLUnderstanding automatic differentiation in PyTorchDeploying trained models and pipelines as a service endpointMonitoring and managing your pipeline's life cycleMeasuring performance improvementsabout the readerFor data professionals with intermediate Python skills and basic familiarity with machine learning. No cloud experience required.about the authorCarl Osipov has spent over 15 years working on big data processing and machine learning in multi-core, distributed systems, such as service-oriented architecture and cloud computing platforms. While at IBM, Carl helped IBM Software Group to shape its strategy around the use of Docker and other container-based technologies for serverless computing using IBM Cloud and Amazon Web Services. At Google, Carl learned from the world's foremost experts in machine learning and also helped manage the company's efforts to democratize artificial intelligence. You can learn more about Carl from his blog Clouds With Carl.

Web components are a set of web platform APIs that allow you to create reusable modular HTML tags for your web apps and pages. With web components, you can easily make your own share buttons, date pickers, and more in a way that makes it easy to customize.