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Taschenbuch. Zustand: Neu. Neuware -'Statistics with Rust, Second Edition' is designed to help you learn quickly, focusing on practical statistics using Rust scripts. The book is for readers who know the basics of statistics and machine learning. It gives quick explanations so you can try out concepts with hands-on coding. The book uses the newest version of Rust, 1.72.0, to help users build and secure statistical and machine learning algorithms. Each chapter is full of useful programs and code examples that will walk you through tasks like data manipulation, statistical tests, regression analysis, building machine learning models, and natural language processing.We've covered great Rust crates featured throughout, including:ndarray and ndarray-linalg: For efficient handling of multi-dimensional arrays and linear algebra operations.ndarray-stats: To perform statistical computations on arrays.rand and rand_distr: For generating random numbers and working with probability distributions.smartcore: A machine learning library used for implementing algorithms like decision trees and random forests.linfa: A toolkit providing implementations of Support Vector Machines and other algorithms.tch: Rust bindings for PyTorch, enabling the creation and training of neural networks.finalfusion: For working with word embeddings in natural language processing tasks.rust-stemmers: To perform stemming in text preprocessing.regex: For pattern matching and text manipulation.unicode-segmentation: To accurately tokenize Unicode strings.This second edition brings all chapters up to date with the latest in stats and Rust programming. It focuses on how you can put these things to practical use, with a detailed look at advanced algorithms like PCA, SVM, neural networks, and ensemble methods. We've also included some natural language processing topics, such as text preprocessing, tokenization, and word embeddings. The book also shows you how to combine Rust's performance and safety with statistical analysis, giving you the tools you need to do data analysis efficiently and reliably. The book's got lots of practical code and explanations that are easy to understand, which helps you learn the skills you need to get to grips with data using Rust.Table of ContentIntroduction to Rust for StatisticiansData Handling and PreprocessingDescriptive StatisticsProbability Distributions and Random VariablesInferential StatisticsRegression AnalysisBayesian StatisticsMultivariate Statistical MethodsNonlinear Models and Machine LearningModel Evaluation and ValidationText and Natural Language ProcessingLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 214 pp. Englisch.

Taschenbuch. Zustand: Neu. Neuware -This is the practical, solution-oriented book for every data scientists, machine learning engineers, and AI engineers to utilize the most of Google JAX for efficient and advanced machine learning. It covers essential tasks, troubleshooting scenarios, and optimization techniques to address common challenges encountered while working with JAX across machine learning and numerical computing projects.The book starts with the move from NumPy to JAX. It introduces the best ways to speed up computations, handle data types, generate random numbers, and perform in-place operations. It then shows you how to use profiling techniques to monitor computation time and device memory, helping you to optimize training and performance. The debugging section provides clear and effective strategies for resolving common runtime issues, including shape mismatches, NaNs, and control flow errors. The book goes on to show you how to master Pytrees for data manipulation, integrate external functions through the Foreign Function Interface (FFI), and utilize advanced serialization and type promotion techniques for stable computations.If you want to optimize training processes, this book has you covered. It includes recipes for efficient data loading, building custom neural networks, implementing mixed precision, and tracking experiments with Penzai. You'll learn how to visualize model performance and monitor metrics to assess training progress effectively. The recipes in this book tackle real-world scenarios and give users the power to fix issues and fine-tune models quickly.Key LearningsGet your calculations done faster by moving from NumPy to JAX's optimized framework.Make your training pipelines more efficient by profiling how long things take and how much memory they use.Use debugging techniques to fix runtime issues like shape mismatches and numerical instability.Get to grips with Pytrees for managing complex, nested data structures across various machine learning tasks.Use JAX's Foreign Function Interface (FFI) to bring in external functions and give your computational capabilities a boost.Take advantage of mixed-precision training to speed up neural network computations without sacrificing model accuracy.Keep your experiments on track with Penzai. This lets you reproduce results and monitor key metrics.Create your own neural networks and optimizers directly in JAX so you have full control of the architecture.Use serialization techniques to save, load, and transfer models and training checkpoints efficiently.Table of ContentTransition NumPy to JAXProfiling Computation and Device MemoryDebugging Runtime Values and ErrorsMastering Pytrees for Data StructuresExporting and SerializationType Promotion Semantics and Mixed PrecisionIntegrating Foreign Functions (FFI)Training Neural Networks with JAXLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 252 pp. Englisch.

Taschenbuch. Zustand: Neu. Neuware -Get certified in Node.js application development and take your career to the next level! The JSNAD certification is your ticket to proving your deep understanding and proficiency in Node.js application development. This fantastic book is perfect for anyone aiming to ace the JSNAD exam.The book is packed with essential Node.js concepts, including asynchronous programming, middleware integration, and advanced routing techniques. You'll learn about testing strategies, deployment methodologies, and performance optimization. This book includes quick reference guides and a glossary of advanced terms, making it easy to revisit key concepts and really cement your understanding. You'll also find lots of practical exercises and knowledge checks throughout the book, which are great for checking your understanding and spotting areas you can improve on. You'll find detailed explanations of complex topics that demystify intricate Node.js functionalities, making them accessible and comprehensible.And there's more! The book also offers access to sample projects and code repositories, providing hands-on experience that mirrors the scenarios encountered in the certification exam. These projects are the perfect chance for learners to put their new skills into practice, building amazing, robust Node.js applications that can scale to any challenge!Key LearningsMaster Node.js architecture and the event-driven, non-blocking I/O model.Master asynchronous programming using callbacks, promises, and async/await.Implement a robust middleware solution for efficient request handling in Express.js.Write unit tests using Mocha and Chai to ensure your code is reliable.Use Jest to test the full range of Node.js applications.Set up secure environment variables for different stages of deployment.Profile and manage your applications' memory to improve performance.Deploy your Node.js applications using PM2 and configure Nginx as a reverse proxy.Create CI/CD pipelines with GitHub Actions for automated testing and deployment.Table of ContentAdvanced Node.js ConceptsModule Systems and Package ManagementProcess Management and System InteractionNetwork Programming and SecurityFile Systems and Data StreamsAdvanced APIs and Utility ModulesPerformance Optimization and TestingLibri GmbH, Europaallee 1, 36244 Bad Hersfeld 308 pp. Englisch.