Bøker utgitt av Manning Publications

Many enterprise applications intertwine code that defines an apps behaviour with code that defines its network communication and other non-functional concerns. The service mesh pattern, implemented by platforms like Istio, helps you push operational issues into the infrastructure so the application code is easier to understand, maintain, and adapt.Istio in Action teaches you how to implement a full-featured Istio-based service mesh to manage a microservices application. With the skills you learn in this comprehensive tutorial, youll be able to delegate the complex infrastructure of your cloud-native applications to Istio!

Customer-facing and internal APIs have become the most common wayto integrate the components of web-based software. Using standards like OpenAPI, you can provide reliable, easy-to-use interfaces that allow other developers safe, controlled access to your software. Designing APIs with Swagger and OpenAPI is a hands-on primer to properly designing and describing your APIs using the most widely-adopted standard.

Engineer privacy into your systems with these hands-on techniques for data governance, legal compliance, and surviving security audits.In Privacy Engineering youwill learn how to:Classify data based on privacy risk Build technical tools to catalog and discover data in your systems Share data with technical privacy controls to measure reidentification risk Implement technical privacy architectures to delete data Set up technical capabilities for data export to meet legal requirements like Data Subject Requests (DSAR) Establish a technical privacy review process to help accelerate the legal Privacy Impact Assessment (PIA) Design a Consent Management Platform (CMP) to capture user consent Implement security tooling to help optimize privacy Build a holistic program that will get support and funding from the C-Level and boardPrivacy Engineering teaches you to implement technical privacy solutions and tools at scale. Youll learn from author Nishant Bhajaria, an industry-renowned expert who has overseen the privacy programs at Google, Netflix, and Uber. Youll find technical methods that can be instantly applied to almost any system, and improve your user privacy without spiraling time and resource costs.

Field-tested tips, tricks, and design patterns for building MachineLearning projects that are deployable, maintainable, and secure from concept toproduction.In Machine Learning Engineering inAction, you will learn: Evaluatingdata science problems to find the most effective solution Scopinga machine learning project for usage expectations and budget Processtechniques that minimize wasted effort and speed up production Assessinga project using standardized prototyping work and statistical validation Choosingthe right technologies and tools for your project Makingyour codebase more understandable, maintainable, and testable Automatingyour troubleshooting and logging practices Databricks solutions architect BenWilson lays out an approach to building deployable, maintainable productionmachine learning systems. YouGÇÖll adopt software development standards thatdeliver better code management, and make it easier to test, scale, and evenreuse your machine learning code!

A practical field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples.In How to Lead in Data Science you'll master techniques for leading data science at every seniority level, from heading up a single project to overseeing a whole company's data strategy. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away.

Practical techniques for writing code that is robust, reliable, and easy for team members to understand and adapt.In Good Code, Bad Code youll learn how to:Think about code like an effective software engineerWrite functions that read like well-structured sentencesEnsure code is reliable and bug freeEffectively unit test codeIdentify code that can cause problems and improve itWrite code that is reusable and adaptable to new requirementsImprove your medium and long-term productivitySave yourself and your team time The difference between good code or bad code often comes down tohow you apply the established practices of the software development community.In Good Code, Bad Code youll learn how to boost your productivity and effectiveness with code development insights normally only learned through careful mentorship and hundreds of code reviews.

Your brain responds in a predictable way when it encounters new or difficult tasks. This unique book teaches you concrete techniques rooted incognitive science that will improve the way you learn and think about code.In The Programmers Brain:What every programmer needs to know about cognition you will learn:Fast and effective ways to master new programming languagesSpeed reading skills to quickly comprehend new code Techniques to unravel the meaning of complex codeWays to learn new syntax and keep it memorizedWriting code that is easy for others to readPicking the right names for your variablesMaking your codebase more understandable to newcomersOnboarding new developers to your teamLearn how to optimize your brains natural cognitive processes to read code more easily, write code faster, and pick up new languages in much less time. This book will help you through the confusion you feel when faced with strange and complex code, and explain a code base inways that can make a new team member productive in days!

Learn how to speed up slow Python code with concurrent programming and the cutting-edge asyncio library.Python is flexible, versatile, and easy to learn. It can also be very slow compared to lower-level languages. Python Concurrency with asyncio teaches you how to boost Python's performance by applying a variety of concurrency techniques. You'll learn how the complex-but-powerful asyncio library can achieve concurrency with just a single thread and use asyncio's APIs to run multiple web requests and database queries simultaneously. The book covers using asyncio with the entire Python concurrency landscape, including multiprocessing and multithreading.

Journey through the theory and practice of modern deep learning, and apply innovative techniques to solve everyday data problems.In Inside Deep Learning, you will learn how to:Implement deep learning with PyTorchSelect the right deep learning componentsTrain and evaluate a deep learning modelFine tune deep learning models to maximize performanceUnderstand deep learning terminologyAdapt existing PyTorch code to solve new problemsInside Deep Learning is an accessible guide to implementing deep learning with the PyTorch framework. It demystifies complex deep learning concepts and teaches you to understand the vocabulary of deep learning so you can keep pace in a rapidly evolving field. No detail is skippedyoull dive into math, theory, and practical applications. Everything is clearly explained in plain English.

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

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