Machine Learning and Rust: From Basics to Production

The book "Machine Learning and Rust" is the first practical guide of its kind, combining the power and performance of the Rust language with modern machine learning methods. The author, Alexey Volkov, an experienced Rust developer and ML researcher, will take you step by step from the basics to creating production-ready models.

This book is intended for:

• Rust developers who want to learn machine learning without switching to Python
• Data Scientists seeking to improve the performance of their models
• Systems programmers interested in embedding ML into high-load systems
• Anyone who wants to understand how to apply Rust in the field of artificial intelligence

What you will learn from the book:

• Fundamentals of linear algebra and statistics using the ndarray and nalgebra crates
• Implementation of classic algorithms: linear regression, decision trees, SVM
• Building and training neural networks using tch-rs (PyTorch binding) and burn
• Working with data: loading, preprocessing, and augmentation using polars and csv
• Model serialization, deployment, and optimization for production
• Parallel computing and GPU acceleration in Rust
• Practical projects: from image recognition to time series

Book features:

• All code examples are available on GitHub and fully functional
• Step-by-step instructions with explanations for each step
• Performance comparison of Rust vs Python in ML tasks
• Tips on choosing crates and application architecture

The book is written in an engaging style and is suitable for both Rust beginners (with basic syntax knowledge) and experienced programmers. After reading it, you will be able to create fast, safe, and efficient ML systems ready for deployment in real-world projects.

An ideal addition to your library if you want to take your machine learning skills to the next level with Rust.