Название: Deep Learning via Rust: State of the Art Deep Learning in RustАвтор: Evan Pradipta Hardinatha, Jaisy Arasy, Idham Hanif Multazam
Издательство: RantAI Academy
Серия: Semi-Numerical Series Book 2
Год: December 22, 2024
Страниц: 109
Язык: английский
Формат: pdf, epub
Размер: 10.1 MB
"Deep Learning via Rust" or DLVR offers a comprehensive exploration of Deep Learning concepts and techniques through the lens of the Rust programming language, known for its performance and safety. The book begins by establishing a strong foundation in Deep Learning principles, mathematical underpinnings, and introduces essential Rust libraries for Machine Learning. It then delves into a wide array of neural network architectures, including CNNs, RNNs, Transformers, GANs, and emerging models like diffusion and energy-based models, providing both theoretical insights and practical implementations. Advanced topics such as hyperparameter optimization, self-supervised learning, reinforcement learning, and model interpretability are thoroughly examined to enhance model performance and understanding. The later sections focus on building, deploying, and scaling Deep Learning models in Rust across various applications like computer vision, natural language processing, and time series analysis, while also addressing scalable and distributed training techniques. Finally, the book explores current and emerging trends in the field, including federated learning, quantum machine learning, ethical considerations in AI, and the development of large language models using Rust, positioning readers at the forefront of Deep Learning research and applications.
DLVR - Deep Learning via Rust is a comprehensive guide to mastering Deep Learning using Rust, a modern programming language renowned for its performance, safety, and concurrency. This book bridges the gap between Deep Learning theory and hands-on implementation, empowering readers to build ef cient, scalable, and innovative solutions in Rust. By covering foundational concepts, advanced architectures, and cutting-edge techniques, DLVR equips students, professionals, and researchers with the tools and knowledge to navigate the ever-evolving eld of Deep Learning. From the mathematical underpinnings of neural networks to the latest trends in Transformer architectures and probabilistic models, this book provides a holistic journey into Deep Learning, all within the context of the Rust ecosystem.
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