Generative AI and Quantum Computing: A Practical Guide

3.5(6개의 평가)
과학 기술

Explaining the fundamentals of both technologies, including concepts like generative models, quantum mechanics, and quantum algorithms. The document then explores how quantum computing can be used to enhance generative AI, focusing on areas like quantum machine learning and the development of quantum generative models. It further discusses the practical implications of these technologies, such as accelerating drug discovery, optimizing supply chains, and enhancing creative content generation

2024. 11. 06.

Generative AI and Quantum Computing: A Practical Guide

Explaining the fundamentals of both technologies, including concepts like generative models, quantum mechanics, and quantum algorithms. The document then explores how quantum computing can be used to enhance generative AI, focusing on areas like quantum machine learning and the development of quantum generative models. It further discusses the practical implications of these technologies, such as accelerating drug discovery, optimizing supply chains, and enhancing creative content generation

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1개의 에피소드

A comprehensive guide for developers who want to build Generative AI applications. The text explains the foundations of Generative AI and introduces AWS Bedrock as a cloud-based platform designed for building these applications. The book outlines how to choose the right Foundational Models, fine-tune them with Low-Rank Adaptation (LoRA) for specific tasks, and write effective prompts to guide the models' output. The book also explores key aspects of building a Generative AI application, such as user interface design, integration with other AWS services, and security considerations.

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6개의 평가

Explaining the fundamentals of both technologies, including concepts like generative models, quantum mechanics, and quantum algorithms. The document then explores how quantum computing can be used to enhance generative AI, focusing on areas like quantum machine learning and the development of quantum generative models. It further discusses the practical implications of these technologies, such as accelerating drug discovery, optimizing supply chains, and enhancing creative content generation