Statistics with Rust, Second Edition

Keiko Nakamura

ISBN 10: 8119177975 ISBN 13: 9788119177974
Verlag: Gitforgits Okt 2024, 2024
Neu Taschenbuch

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Beschreibung:

This item is printed on demand - Print on Demand Titel. 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. Bestandsnummer des Verkäufers 9788119177974

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Inhaltsangabe:

"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 Content

  1. Introduction to Rust for Statisticians
  2. Data Handling and Preprocessing
  3. Descriptive Statistics
  4. Probability Distributions and Random Variables
  5. Inferential Statistics
  6. Regression Analysis
  7. Bayesian Statistics
  8. Multivariate Statistical Methods
  9. Nonlinear Models and Machine Learning
  10. Model Evaluation and Validation
  11. Text and Natural Language Processing

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Bibliografische Details

Titel: Statistics with Rust, Second Edition
Verlag: Gitforgits Okt 2024
Erscheinungsdatum: 2024
Einband: Taschenbuch
Zustand: Neu
Auflage: 2. Auflage

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Nakamura, Keiko
Verlag: GitforGits, 2024
ISBN 10: 8119177975 ISBN 13: 9788119177974
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