Machine Learning with Rust - 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 Learnings

• Exploit 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 Content

1. Rust Basics for Machine Learning
2. Data Wrangling with Rust
3. Linear Regression by Example
4. Logistic Regression for Classification
5. Decision Trees in Action
6. Mastering Random Forests
7. Support Vector Machines in Action
8. Simplifying Naive Bayes and k-NN
9. Crafting Neural Networks with Rust