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Full Description
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.
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.
