Bøger af Keiko Nakamura

In this stimulating journey of Rust, you'll learn how to use the Rust programming language in conjunction with machine learning. It's not a full guide to learning machine learning with Rust. Instead, it's more of a journey that shows you what's possible when you use Rust to solve machine learning problems. Some people like Rust because it is quick and safe. This book shows how those qualities can help machine learning a lot.To begin, we will show you what Rust is and how it works. This is so that everyone, even those who are new to Rust, can follow along. Then, we look at some basic machine learning concepts, such as linear and logistic regression, and show how to use Rust's tools and libraries to make these ideas work.You will learn more complex techniques like decision trees, support vector machines, and how to work with data as we go along. It goes all the way up to neural networks and image recognition, and we show you how to use Rust for these types of tasks step by step. We use real-world examples, such as COVID data and the CIFAR-10 image set, to show how Rust works with issues that come up in the real world.This book is all about discovery and experimentation. To see what you can do with them, we use various Rust tools for machine learning. It's a fun way to see how Rust can be used in machine learning, and it will make you want to try new things and learn more on your own. This is only the beginning; there is so much more to uncover as you continue to explore machine learning with Rust. Key LearningsExploit Rust's efficiency and safety to construct fast machine learning models.Use Rust's ndarray crate for numerical computations to manipulate complex machine learning data.Find out how Rust's extensible machine learning framework, linfa, works across algorithms.Use Rust's precision and speed to construct linear and logistic regression.See how Rust crates simplify decision trees and random forests for prediction and categorization.Learn to implement and optimize probabilistic classifiers, SVMs and closest neighbor methods in Rust.Use Rust's computing power to study neural networks and CNNs for picture recognition and processing.Apply learnt strategies to COVID and CIFAR-10 datasets to address realistic problems and obtain insights. Table of ContentRust Basics for Machine LearningData Wrangling with RustLinear Regression by ExampleLogistic Regression for ClassificationDecision Trees in ActionMastering Random ForestsSupport Vector Machines in ActionSimplifying Naive Bayes and k-NNCrafting Neural Networks with Rust

Are you an experienced statistician or data professional looking for a powerful, efficient, and versatile programming language to turbocharge your data analysis and machine learning projects? Look no further! "Statistics with Rust" is your comprehensive resource to unlock Rust's true potential in modern statistical methods.¿¿This book is tailored specifically for statisticians and data professionals who are already familiar with the fundamentals of statistics and want to leverage the speed and reliability of Rust in their projects. Over 11 in-depth chapters, you will discover how Rust outperforms Python in various aspects of data analysis and machine learning and learn to implement popular statistical methods using Rust's unique features and libraries."Statistics with Rust" begins by introducing you to Rust's programming environment and essential libraries for data professionals. You'll then dive into data handling, preprocessing, and visualization techniques that form the backbone of any statistical analysis. As you progress through the book, you'll explore descriptive and inferential statistics, probability distributions, regression analysis, time series analysis, Bayesian statistics, multivariate statistical methods, and nonlinear models. Additionally, the book covers essential machine-learning techniques, model evaluation and validation, natural language processing, and advanced techniques in emerging topics.To ensure you get the most out of this book, each chapter includes hands-on examples and exercises to reinforce your understanding of the concepts presented. You'll also learn to optimize your Rust code and select the best tools and libraries for each task, maximizing your productivity and efficiency.Key LearningsDiscover Rust's unique advantages for statistical analysis and machine learning projects.Learn to efficiently handle, preprocess, and visualize data using Rust libraries.Implement descriptive and inferential statistics with Rust for powerful data insights.Master probability distributions and random variables in Rust for robust simulations.Perform advanced regression analysis with Rust's capabilities.Explore Bayesian statistics and Markov Chain Monte Carlo methods in Rust.Uncover multivariate techniques, including PCA and Factor Analysis, using Rust libraries.Implement cutting-edge machine learning algorithms and model evaluation techniques in Rust.Delve into text analysis, natural language processing, and network analysis with Rust.