Deep Learning via Rust State of the Art Deep Learning in Rust

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Deep Learning via Rust: State of the Art Deep Learning in Rust by Evan Pradipta Hardinatha, Jaisy Arasy, Idham Hanif Multazam
English | December 22, 2024 | ISBN: N/A | ASIN: B0DR8HPX7P | 107 pages | PDF | 2.11 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.



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