Deep Learning with Rust : Mastering Efficient and Safe Neural Networks in the Rust Ecosystem / by Mehrdad Maleki.

Format:

Book

Author/Creator:

Maleki, Mehrdad.

Series:

Professional and Applied Computing Series

Language:

English

Subjects (All):

Programming languages (Electronic computers).

Artificial intelligence.

Computer programming.

Programming Language.

Artificial Intelligence.

Programming Techniques.

Local Subjects:

Programming Language.

Artificial Intelligence.

Programming Techniques.

Physical Description:

1 online resource (201 pages)

Edition:

1st ed. 2026.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2026.

Summary:

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. You Will Understand deep learning foundations and Rust programming principles. Implement and optimize deep learning models in Rust using tools and libraries available. 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.

Contents:

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.

Notes:

Description based upon print version of record.

Description based on publisher supplied metadata and other sources.

ISBN:

979-88-6882-208-7

OCLC:

1573145567