Böcker utgivna av Manning Publications

Julia as a Second Language makes it easy to add Julia to your programming toolbox. You will learn about Julia's type system and data structures by modeling the launch of a space rocket, use dictionaries to parse Roman numerals, and discover tuples and arrays through tracking pizza sales. You will even use Julia's unique multiple dispatch feature to send knights and archers into a simulated battle. By the time you are finished, you will be confident in the foundations of Julia and ready to dive into a specialised field like machine learning or data science. Don't be put off by Julia's reputation as a scientific programming language. There's no data science or numerical computing knowledge required. You can get started with what you learned in high school math classes. Research scientists, data analysts and machine learning professionals will appreciate how easy Julia makes it to handle and explore data sets. Experienced programmers will love to learn about Julia's interesting metaprogramming capabilities and the exciting possibilities of the language.

How To Read Java: Understanding, debugging, and optimizing JVM applications teaches you how to better understand Java application code. It's filled with code investigation techniques for tricky issues like uncovering glitches in code logic or locating intermittent runtime problems. Armed with these tools and practices you'll save time whenever you need to identify performance issues, understand dependencies, discover the root cause of crashes, or interpret unexpected results. Whether you're a senior software engineer or you're just getting started, this invaluable guide will help you in the fundamental task of understanding how your applications work. For intermediate Java developers.

Build predictive models from time-based patterns in your data. Master statistical models including new deep learning approaches for time series forecasting.In Time Series Forecasting in Python you will learn how to:Recognize a time series forecasting problem and build a performant predictive modelCreate univariate forecasting models that account for seasonal effects and external variablesBuild multivariate forecasting models to predict many time series at onceLeverage large datasets by using deep learning for forecasting time seriesAutomate the forecasting processTime Series Forecasting in Python teaches you to build powerful predictive models from time-based data. Every model you create is relevant, useful, and easy to implement with Python. You'll explore interesting real-world datasets like Google's daily stock price and economic data for the USA, quickly progressing from the basics to developing large-scale models that use deep learning tools like TensorFlow.

Use Akka to solve the big problems of distributed systemsfrom multithreading and concurrency, to handling scalability and failure.In Akka in Action, Second Edition you will learn how to:Create basic programs with AkkaWork with clusters to build robust, fault tolerant programsCreate and maintain distributed state with strong consistency guaranteesBuild microservices with AkkaUtilize concurrency and parallelismTest Akka softwareAkka in Action, Second Edition teaches you to use the latest version of Akka to solve common problems of distributed systems. Akka contributor Francisco Lï¿¿pez-Sancho demonstrates Akkas complex concepts through real-world use cases, including clustering, sharding, persistence, and deploying to Kubernetes. Discover the power of the Actor model, and how to leverage most of the Akka modules to create microservices that are reliable and fault tolerant.about the technologyAkka is a toolkit of libraries that make it easy to implement distributed applications in Scala and Java. Akkas Actor model avoids many of the complexities of multithreading, while making systems elastic and resilient, and provides strong consistency.

Master Java persistence using the industry-leading tools Spring Data and Hibernate.In Java Persistence with Spring Data and Hibernate you will learn: Mapping persistent classes, value types, and inheritance Mapping collections and entity associations Processing transactions with Spring Data and Hibernate Creating fetch plans, strategies, and profiles Filtering data Building Spring Data REST projects Using Java persistence with non-relational databases Querying JPA with QueryDSL Testing Java persistence applications Java Persistence with Spring Data and Hibernate teaches you the ins-and-outs of Java persistence with hands-on examples using Spring Data, JPA, and Hibernate. The book carefully analyzes the capabilities of the major Java persistence tools, and guides you through the most common use cases. By comparing and contrasting the alternatives, you’ll find it easy to choose the right tool choice for your applications. You’ll learn how to make and utilize mapping strategies, about the different approach to transactions for both Hibernate and Spring Data, and even how to efficiently test Java persistence applications. The practical techniques are demonstrated with both relational and non-relational databases. Forewords by Dmitry Aleksandrov and Mohamed Taman. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Effectively managing application data is essential for any serious application. Spring Data and Hibernate bridge the gap between object-oriented code and relational data stores, radically simplifying Java persistence. By implementing the Java Persistence API (JPA) standard, these powerful tools help you avoid common bugs related to state and application data storage. About the book Java Persistence with Spring Data and Hibernate explores Java persistence using industry-standard tools. Hands-on examples introduce object-relational mapping and guide you through different mapping strategies to suit your needs. Covering transactions, persistent application testing, and non-relational databases, this book is your go-to resource for managing data in Java applications. What's inside Mapping persistent classes, value types, and inheritance Creating fetch plans, strategies, and profiles Building Spring Data REST projects Querying JPA with QueryDSL About the reader For intermediate Java programmers. About the author Catalin Tudose has more than 20 years of experience in the Java community. Christian Bauer, Gavin King, and Gary Gregory are the authors of Java Persistence with Hibernate, Second Edition, on which this book is based. Table of Contents PART 1 - GETTING STARTED WITH ORM 1 Understanding object/relational persistence 2 Starting a project 3 Domain models and metadata 4 Working with Spring Data JPA PART 2 - MAPPING STRATEGIES 5 Mapping persistent classes 6 Mapping value types 7 Mapping inheritance 8 Mapping collections and entity associations 9 Advanced entity association mappings PART 3 - TRANSACTIONAL DATA PROCESSING 10 Managing data 11 Transactions and concurrency 12 Fetch plans, strategies, and profiles 13 Filtering data PART 4 - BUILDING JAVA PERSISTENCE APPLICATIONS WITH SPRING 14 Integrating JPA and Hibernate with Spring 15 Working with Spring Data JDBC 16 Working with Spring Data REST PART 5 - BUILDING JAVA PERSISTENCE APPLICATIONS WITH SPRING 17 Working with Spring Data MongoDB 18 Working with Hibernate OGM PART 6 - WRITING QUERIES AND TESTING JAVA PERSISTENCE APPLICATIONS 19 Querying JPA with Querydsl 20 Testing Java persistence applications

Create bulletproof, high-performance web apps and servers with Rust.In Rust Web Development you will learn:Handling the borrow checker in an asynchronous environmentCreating web APIs and using JSON in RustGraceful error handlingTesting, tracing, logging, and debuggingDeploying Rust applicationsEfficient database accessRust Web Development is a hands-on guide to building server-based web applications with Rust. If youve built web servers using Java, C#, or PHP, youll instantly fall in love with the performance and development experience Rust delivers. This book shows you how to work efficiently using pure Rust, along with important Rust libraries such as tokio for async runtimes, warp for web servers and APIs, and reqwest to run external HTTP requests.

You know how to build Go programsGÇönow learn how to ship them to your customers efficiently! This practical guide to continuous delivery shows you how to rapidly establish an automated pipeline that will improve your testing, code quality, and final product.Bootstrapping Go is a hands-on guide to shipping Go-based software. Following examples in the powerful Go programming language, youGÇÖll learn how to establish pipelines that seamlessly ferry your projects through production and deployment. Put the theory of continuous delivery and continuous integration into action, and discover instantly useful guidance on automating your teamGÇÖs build and reacting with agility to customer demands. Development pipelines built to the principles of continuous delivery are the best way for code to flow through your organization. A properly functioning pipeline makes it seamless to modify functionality, enhance code quality, and evolve your deployments to meet your customerGÇÖs needs.

Securing Kubernetes Secrets is a compact hands-on guide to storing and protecting sensitive data in a Kubernetes environment. YouGÇÖll learn a security-first mindset that is vital for storing and using secrets correctly, and tools and concepts that will help you manage sensitive assets such as certificates, keys, and key rotation. Throughout, youGÇÖll tinker with relevant real-world examples. Protect secrets stored in a source code repository and consume them in a Kubernetes cluster, deploy HashiCorp Vault to secure KubernetesGÇÖs underlying infrastructure components and applications running on the platform, along with adding layers of security to maintain protection even in the event of a partial breach. By the time youGÇÖre finished, youGÇÖll have a toolbox of techniques that you can apply both as a developer or operations professional.

Math and Architectures of Deep Learning bridges the gap between theory and practice, laying out the math of deep learning side by side with practical implementations in Python and PyTorch. YouGÇÖll peer inside the GÇ£black boxGÇ¥ to understand how your code is working, and learn to comprehend cutting-edge research you can turn into practical applications. Math and Architectures of Deep Learning sets out the foundations of DL usefully and accessibly to working practitioners. Each chapter explores a new fundamental DL concept or architectural pattern, explaining the underpinning mathematics and demonstrating how they work in practice with well-annotated Python code. YouGÇÖll start with a primer of basic algebra, calculus, and statistics, working your way up to state-of-the-art DL paradigms taken from the latest research. Learning mathematical foundations and neural network architecture can be challenging, but the payoff is big. YouGÇÖll be free from blind reliance on pre-packaged DL models and able to build, customize, and re-architect for your specific needs. And when things go wrong, youGÇÖll be glad you can quickly identify and fix problems.

In Effective Software Testing you will learn how to: Engineer tests with a much higher chance of finding bugs Read code coverage metrics and use them to improve your test suite Understand when to use unit tests, integration tests, and system tests Use mocks and stubs to simplify your unit testing Think of pre-conditions, post-conditions, invariants, and contracts Implement property-based tests Utilize coding practices like dependency injection and hexagonal architecture that make your software easier to test Write good and maintainable test code Effective Software Testing teaches you a systematic approach to software testing. YouGÇÖll master easy-to-apply techniques to create strong test suites that are specifically engineered for your code. Following real-world use cases and detailed code samples, youGÇÖll soon be engineering tests that find the bugs hiding in edge cases and the parts of code you would never think of testing! Along the way, youGÇÖll develop an intuition for testing that can save years of learning by trial and error. Go beyond unit tests! Great software testing makes the entire development process more efficient, from understanding your code before you write it to catching bugs in tricky corner cases.Effective Software Testing teaches you a systematic approach to software testing. YouGÇÖll master easy-to-apply techniques to create strong test suites that are specifically engineered for your code. Following real-world use cases and detailed code samples, youGÇÖll soon be engineering tests that find the bugs hiding in edge cases and the parts of code you would never think of testing! Along the way, youGÇÖll develop an intuition for testing that can save years of learning by trial and error.

Spot errors in your Go code you didnGÇÖt even know you were making and boost your productivity by avoiding common mistakes and pitfalls. 100 Go Mistakes: How to Avoid Them shows you how to: Dodge the most common mistakes made by Go developers Structure and organize your Go application Handle data and control structures efficiently Deal with errors in an idiomatic manner Improve your concurrency skills Optimize your code Make your application production-ready and improve testing quality 100 Go Mistakes: How to Avoid Them puts a spotlight on common errors in Go code you might not even know youGÇÖre making. YouGÇÖll explore key areas of the language such as concurrency, testing, data structures, and moreGÇöand learn how to avoid and fix mistakes in your own projects.

Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, youGÇÖll master scalable techniques for data storage, computation, experiment tracking, and orchestration. YouGÇÖll also learn how to collaborate with data scientists to deliver exactly what they need to succeed. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science.

Build backend servers, services, and front-ends in Rust to get fast, reliable, and maintainable applications. Rust Servers, Services, and Apps teaches you how to build web servers, RESTful services, server-rendered apps, and client front-ends in Rust. YouGÇÖll learn to write code with small and predictable resource footprints, and build high-performing applications with unmatched safety and reliability. In Rust Servers, Services, and Apps, youGÇÖll learn: Developing database-backed web services in Rust Building and securing RESTful APIs Writing server-side web applications in Rust Measuring and benchmarking web service performance Packaging and deploying web services Full-stack Rust applications

This practical guide to building deep learning models with the new features of TensorFlow 2.0 is filled with engaging projects, simple language, and coverage of the latest algorithms.In TensorFlow 2.0 in Action, you'll dig into the newest version of Google's amazing TensorFlow framework as you learn to create incredible deep learning applications. You'll develop a sentiment analyzer for movie reviews, an NLP spam classifier, and other hands-on projects.