Machine Learning Rust Second by Nakamura Keiko (19 results)

Paperback or Softback. Condition: New. Statistics with Rust, Second Edition: Explore rust programming and its powerful crates across data science, machine learning and NLP projects. Book.

Paperback or Softback. Condition: New. Machine Learning with Rust, Second Edition: Implement data pipelines, classical models, deep learning and NLP using burn, candle, linfa and smartcore. Book.

Condition: New.

Condition: New.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Condition: New. In English.

Paperback. Condition: new. Paperback. This machine learning book is a new edition out now for Rust developers to build practical machine learning systems without getting bogged down in the complexities of Rust 1.85. We put together a single workspace over eleven chapters, building it from a Polars-based data pipeline through classical models, deep neural networks, and natural language processing, right up to a deployed REST API.We teach you to make use of linfa and smartcore crates for regression, classification, ensemble methods, and support vector machines. We build and train neural networks using the Burn framework, work with convolutional architectures on image data, and load pre-trained transformer models through Candle. We also use a lightweight NLP pipeline with TF-IDF and Gaussian Naive Bayes from first principles. At all times, Rust's ownership model and type system work together to make sure things are correct, rather than getting in the way.This particular book is a perfect knowledge source for developers who already know Rust at a beginner's level and want to use that knowledge for machine learning tasks. You don't need to have worked with any ML frameworks before.Key LearningsStructure a multi-crate Rust workspace for end-to-end machine learning.Build type-safe data pipelines using Polars and Apache Arrow.Train, evaluate, and improve classical models with linfa and smartcore.Implement backpropagation and mini-batch gradient descent.Train multilayer perceptrons and convolutional networks using Burn.Load and fine-tune pre-trained BERT models using Candle.Apply LoRA weight adaptation to transformer layers.Construct production TF-IDF and Naive Bayes NLP pipeline.Serve trained models using axum and tokio.Implement live model hot-reloading and request batching without server downtime.Table of ContentWhy Rust for Machine Learning?Data Engineering with Polars and ArrowRegression and ClassificationDecision Trees, Random Forests, and Gradient BoostingSVMs, Naive Bayes, and k-NNNeural Networks from First PrinciplesDeep Learning with BurnComputer Vision Pipelines with BurnTransformer Inference and Fine-Tuning with CandleNatural Language Processing in RustModel Serving and REST APIs This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: New. Print on Demand.

Condition: New. Print on Demand 2nd ed. edition NO-PA16APR2015-KAP.

Condition: New. PRINT ON DEMAND.

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 296 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - '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 Processing.…

Paperback. Condition: new. Paperback. This machine learning book is a new edition out now for Rust developers to build practical machine learning systems without getting bogged down in the complexities of Rust 1.85. We put together a single workspace over eleven chapters, building it from a Polars-based data pipeline through classical models, deep neural networks, and natural language processing, right up to a deployed REST API.We teach you to make use of linfa and smartcore crates for regression, classification, ensemble methods, and support vector machines. We build and train neural networks using the Burn framework, work with convolutional architectures on image data, and load pre-trained transformer models through Candle. We also use a lightweight NLP pipeline with TF-IDF and Gaussian Naive Bayes from first principles. At all times, Rust's ownership model and type system work together to make sure things are correct, rather than getting in the way.This particular book is a perfect knowledge source for developers who already know Rust at a beginner's level and want to use that knowledge for machine learning tasks. You don't need to have worked with any ML frameworks before.Key LearningsStructure a multi-crate Rust workspace for end-to-end machine learning.Build type-safe data pipelines using Polars and Apache Arrow.Train, evaluate, and improve classical models with linfa and smartcore.Implement backpropagation and mini-batch gradient descent.Train multilayer perceptrons and convolutional networks using Burn.Load and fine-tune pre-trained BERT models using Candle.Apply LoRA weight adaptation to transformer layers.Construct production TF-IDF and Naive Bayes NLP pipeline.Serve trained models using axum and tokio.Implement live model hot-reloading and request batching without server downtime.Table of ContentWhy Rust for Machine Learning?Data Engineering with Polars and ArrowRegression and ClassificationDecision Trees, Random Forests, and Gradient BoostingSVMs, Naive Bayes, and k-NNNeural Networks from First PrinciplesDeep Learning with BurnComputer Vision Pipelines with BurnTransformer Inference and Fine-Tuning with CandleNatural Language Processing in RustModel Serving and REST APIs This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This machine learning book is a new edition out now for Rust developers to build practical machine learning systems without getting bogged down in the complexities of Rust 1.85. We put together a single workspace over eleven chapters, building it from a Polars-based data pipeline through classical models, deep neural networks, and natural language processing, right up to a deployed REST API.We teach you to make use of linfa and smartcore crates for regression, classification, ensemble methods, and support vector machines. We build and train neural networks using the Burn framework, work with convolutional architectures on image data, and load pre-trained transformer models through Candle. We also use a lightweight NLP pipeline with TF-IDF and Gaussian Naive Bayes from first principles. At all times, Rust's ownership model and type system work together to make sure things are correct, rather than getting in the way.This particular book is a perfect knowledge source for developers who already know Rust at a beginner's level and want to use that knowledge for machine learning tasks. You don't need to have worked with any ML frameworks before.Key LearningsStructure a multi-crate Rust workspace for end-to-end machine learning.Build type-safe data pipelines using Polars and Apache Arrow.Train, evaluate, and improve classical models with linfa and smartcore.Implement backpropagation and mini-batch gradient descent.Train multilayer perceptrons and convolutional networks using Burn.Load and fine-tune pre-trained BERT models using Candle.Apply LoRA weight adaptation to transformer layers.Construct production TF-IDF and Naive Bayes NLP pipeline.Serve trained models using axum and tokio.Implement live model hot-reloading and request batching without server downtime.Table of ContentWhy Rust for Machine Learning Data Engineering with Polars and ArrowRegression and ClassificationDecision Trees, Random Forests, and Gradient BoostingSVMs, Naive Bayes, and k-NNNeural Networks from First PrinciplesDeep Learning with BurnComputer Vision Pipelines with BurnTransformer Inference and Fine-Tuning with CandleNatural Language Processing in RustModel Serving and REST APIs.…

Paperback. Condition: new. Paperback. This machine learning book is a new edition out now for Rust developers to build practical machine learning systems without getting bogged down in the complexities of Rust 1.85. We put together a single workspace over eleven chapters, building it from a Polars-based data pipeline through classical models, deep neural networks, and natural language processing, right up to a deployed REST API.We teach you to make use of linfa and smartcore crates for regression, classification, ensemble methods, and support vector machines. We build and train neural networks using the Burn framework, work with convolutional architectures on image data, and load pre-trained transformer models through Candle. We also use a lightweight NLP pipeline with TF-IDF and Gaussian Naive Bayes from first principles. At all times, Rust's ownership model and type system work together to make sure things are correct, rather than getting in the way.This particular book is a perfect knowledge source for developers who already know Rust at a beginner's level and want to use that knowledge for machine learning tasks. You don't need to have worked with any ML frameworks before.Key LearningsStructure a multi-crate Rust workspace for end-to-end machine learning.Build type-safe data pipelines using Polars and Apache Arrow.Train, evaluate, and improve classical models with linfa and smartcore.Implement backpropagation and mini-batch gradient descent.Train multilayer perceptrons and convolutional networks using Burn.Load and fine-tune pre-trained BERT models using Candle.Apply LoRA weight adaptation to transformer layers.Construct production TF-IDF and Naive Bayes NLP pipeline.Serve trained models using axum and tokio.Implement live model hot-reloading and request batching without server downtime.Table of ContentWhy Rust for Machine Learning?Data Engineering with Polars and ArrowRegression and ClassificationDecision Trees, Random Forests, and Gradient BoostingSVMs, Naive Bayes, and k-NNNeural Networks from First PrinciplesDeep Learning with BurnComputer Vision Pipelines with BurnTransformer Inference and Fine-Tuning with CandleNatural Language Processing in RustModel Serving and REST APIs This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condition: Neu. Statistics with Rust, Second Edition | Explore rust programming and its powerful crates across data science, machine learning and NLP projects | Keiko Nakamura | Taschenbuch | Englisch | 2024 | GitforGits | EAN 9788119177974 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware 296 pp. Englisch.

Taschenbuch. Condition: Neu. Machine Learning with Rust, Second Edition | Implement data pipelines, classical models, deep learning and NLP using burn, candle, linfa and smartcore | Keiko Nakamura | Taschenbuch | Englisch | 2026 | GitforGits | EAN 9789349174214 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …