73 Results for : testable

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    Accelerating Angular Development with Ivy
    Accelerating Angular Development with Ivy ab 50.99 € als Taschenbuch: A practical guide to building faster and more testable Angular apps with the new Ivy engine. Aus dem Bereich: Bücher, Ratgeber, Computer & Internet,
    • Shop: hugendubel
    • Price: 50.99 EUR excl. shipping
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    Hands-On Functional Programming with TypeScript
    Hands-On Functional Programming with TypeScript ab 29.49 € als epub eBook: Explore functional and reactive programming to create robust and testable TypeScript applications. Aus dem Bereich: eBooks, Belletristik, Erzählungen,
    • Shop: hugendubel
    • Price: 29.49 EUR excl. shipping
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    Microservice Patterns and Best Practices
    Microservice Patterns and Best Practices ab 41.49 € als epub eBook: Explore patterns like CQRS and event sourcing to create scalable maintainable and testable microservices. Aus dem Bereich: eBooks, Belletristik, Erzählungen,
    • Shop: hugendubel
    • Price: 41.49 EUR excl. shipping
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    Go Programming Cookbook
    Go Programming Cookbook ab 32.99 € als epub eBook: Over 85 recipes to build modular readable and testable Golang applications across various domains 2nd Edition. Aus dem Bereich: eBooks, Belletristik, Erzählungen,
    • Shop: hugendubel
    • Price: 32.99 EUR excl. shipping
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    Einstein's Intuition: Visualizing Nature in Eleven Dimensions , Hörbuch, Digital, ungekürzt, 1231min
    Presented in clear and accessible language, Roberts offers the listener a voyage through the stages of human knowledge. He then examines the outstanding mysteries of modern physics, the phenomena that lie outside the borders of our current understanding (dark energy, dark matter, the Big Bang, wave-particle duality, quantum tunneling, state vector reduction, etc.) and suggests that the next step in our intellectual journey is to treat the vacuum of space as a superfluid - modeling it as being composed of interactive quanta, which, in a self-similar way, are composed of subquanta, and so on. With this proposition, Roberts imbues the vacuum with fractal geometry and opens the door to explaining the outstanding mysteries of physics geometrically. Roberts' model, called quantum space theory, has been praised for how it offers an intuitively accessible picture of 11 dimensions and for powerfully extending the insight of general relativity, eloquently translating the four forces into unique kinds of geometric distortions while offering us access to the underlying deterministic dynamics that give rise to quantum mechanics. That remarkably simple picture explains the mysteries of modern physics in a way that's fully commensurate with Einstein's Intuition. It's a refreshingly unique perspective that generates several testable predictions. ungekürzt. Language: English. Narrator: Jonathan Farkasofsky. Audio sample: https://samples.audible.de/bk/acx0/044645/bk_acx0_044645_sample.mp3. Digital audiobook in aax.
    • Shop: Audible
    • Price: 9.95 EUR excl. shipping
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    Machine Learning Engineering in Action (eBook, ePUB)
    Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists familiar with supervised machine learning and the basics of object-orientaFor data scientists who know machine learning and the basics of object-oriented programming.ed programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it's so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure
    • Shop: buecher
    • Price: 42.40 EUR excl. shipping
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    Hands-On Data Structures and Algorithms with JavaScript
    Hands-On Data Structures and Algorithms with JavaScript ab 41.99 € als epub eBook: Write efficient code that is highly performant scalable and easily testable using JavaScript. Aus dem Bereich: eBooks, Belletristik, Erzählungen,
    • Shop: hugendubel
    • Price: 41.99 EUR excl. shipping
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    Property Testing (eBook, PDF)
    This book introduces important results and techniques in property testing, where the goal is to design algorithms that decide whether their input satisfies a predetermined property in sublinear time, or even in constant time - that is, time is independent of the input size. This book consists of three parts. The first part provides an introduction to the foundations of property testing. The second part studies the testing of specific properties on strings, graphs, functions, and constraint satisfaction problems. Vectors and matrices over real numbers are also covered. The third part is more advanced and explains general conditions, including full characterizations, under which properties are constant-query testable. The first and second parts of the book are intended for first-year graduate students in computer science. They should also be accessible to undergraduate students with the adequate background. The third part can be used by researchers or ambitious graduate students who want to gain a deeper theoretical understanding of property testing.
    • Shop: buecher
    • Price: 73.95 EUR excl. shipping
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    Hands-On RESTful Web Services with ASP.NET Core 3
    Hands-On RESTful Web Services with ASP.NET Core 3 ab 25.99 € als epub eBook: Design production-ready testable and flexible RESTful APIs for web applications and microservices. Aus dem Bereich: eBooks, Belletristik, Erzählungen,
    • Shop: hugendubel
    • Price: 25.99 EUR excl. shipping
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    Machine Learning Engineering in Action
    Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists familiar with supervised machine learning and the basics of object-orientaFor data scientists who know machine learning and the basics of object-oriented programming.ed programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer. Table of Contents PART 1 AN INTRODUCTION TO MACHINE LEARNING ENGINEERING 1 What is a machine learning engineer? 2 Your data science could use some engineering 3 Before you model: Planning and scoping a project 4 Before you model: Communication and logistics of projects 5 Experimentation in action: Planning and researching an ML project 6 Experimentation in action: Testing and evaluating a project 7 Experimentation in action: Moving from prototype to MVP 8 Experimentation in action: Finalizing an MVP with MLflow and runtime optimization PART 2 PREPARING FOR PRODUCTION: CREATING MAINTAINABLE ML 9 Modularity for ML: Writing testable and legible code 10 Standards of coding and creating maintainable ML code 11 Model measurement and why it's so important 12 Holding on to your gains by watching for drift 13 ML development hubris PART 3 DEVELOPING PRODUCTION MACHINE LEARNING CODE 14 Writing production code 15 Quality and acceptance testing 16 Production infrastructure
    • Shop: buecher
    • Price: 59.99 EUR excl. shipping

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