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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.
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
Über den Autor / die Autorin
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.
Zusammenfassung
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 forA 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.
