Maleki Deep Learning with Rust

Mastering Efficient and Safe Neural Networks in the Rust Ecosystem
1. Auflage 2026
ISBN: 979-8-8688-2208-7
Verlag: APRESS
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 182 Seiten

Reihe: Professional and Applied Computing (R0)

Navigate the intersection of deep learning technologies and Rust programming with Deep Learning with Rust. This groundbreaking book is predicated on the idea that the future of efficient, reliable, and secure deep learning applications lies in leveraging Rust's unparalleled features, such as its guarantee of memory safety, concurrency without data races, and abstractions that don't compromise performance.

You’ll explore basic neural concepts through the construction and deployment of sophisticated models using Rust. The language’s advantages for deep learning are emphasized, including enhanced performance, safety, and scalability. Through a unique blend of theory and application, you’ll explore how Rust can address the growing demand for technologies that can ensure faster, more secure deep learning solutions in an era where data volume and complexity are increasing exponentially.

This book meets a pressing need at a time when the integration of deep learning is critical across many diverse sectors. Programming languages can not only accelerate the development of AI models but also ensure they are built on a foundation of security and efficiency. This book is an indispensable resource for anyone looking to master the art of building next-generation deep learning with Rust’s growing ecosystem

What You Will Learn

• Understand deep learning foundations and Rust programming principles.
• Implement and optimize deep learning models in Rust, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs.
• Develop practical deep learning applications to solve real-world problems, including natural language processing, computer vision, and speech recognition.
• Explore Rust’s safety features, including its strict type of system and ownership model, and learn strategies to create reliable and secure AI software.
• Gain an understanding of the broader ecosystem of tools and libraries available for deep learning in Rust.

Who This Book Is for

A broad audience with varying levels of experience and knowledge, including advanced programmers with a solid foundation in Rust or other programming languages (Python, C++, and Java) who are interested in learning how Rust can be used for deep learning apps. It may also be suitable for data scientists and AI practitioners who are looking to understand how Rust can enhance the performance and safety of deep learning models, even if they are new to the Rust programming language.

Zielgruppe

Popular/general

Autoren/Hrsg.

Maleki, Mehrdad

Inhaltsverzeichnis

Part I: Foundations of Deep Learning in Rust .- Chapter 1: Introduction.- Chapter 2: Introduction to Deep Learning in Rust.- Chapter 3: Rust Syntax for AI Practitioners (Optional).- Chapter 4: Why Rust for Deep Learning?.- Part II: Advancing with Rust in AI.-   Chapter 5: Building Blocks of Neural Networks in Rust .- Chapter 6: Rust Concurrency in AI - Chapter 7: Deep Neural Networks and Advanced Architectures .- Chapter 8: Generative Models and Transformers in Rust.

Über Autor(innen)

Dr. Mehrdad Maleki holds a Ph.D. in Theoretical Computer Science and a Master’s degree in Mathematics. He is an accomplished AI Scientist and researcher specializing in artificial intelligence, quantum computing, and cybersecurity. His work combines deep mathematical insight with practical engineering to design scalable, high-performance AI and quantum systems.

Over the years, Dr. Maleki has led several R&D projects, contributing to more than ten patents in AI and quantum computing. His research and innovations span areas such as deep learning, foundation models, automatic differentiation, and scientific computing. Proficient in Python and Rust, he bridges the gap between theoretical research and real-world applications by transforming complex algorithms into impactful solutions.