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Neelesh Yadav

As seen in: Amazon Web Services, MDPI
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Articles

A Prototype Forest Fire Decision Support System for Uttarakhand, India

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By Neelesh Yadav, Shrey Rakholia, Peter Moore, Laura Patricia Ponce-Calderón
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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A Prototype Decision Support System for Tree Selection and Plantation with a Focus on Agroforestry and Ecosystem Services

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By Neelesh Yadav, Shrey Rakholia, Reuven Yosef
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
Open in Who Shared Issue with article?

Best Practices from Quantiphi for Unleashing Generative AI Functionality by Fine-Tuning LLMs | Amazon Web Services

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By Neelesh Yadav, Stephanie Pace, Matt Chastain
| Amazon Web Services
AWS Partner Network (APN) Blog By Neelesh Kumar Yadav, AI/ML Architect – Quantiphi By Stephanie Pace, AI/ML Practice Leader – Quantiphi By Matt Chastain, Sr. Solutions Architect, AWS Generative artificial intelligence (AI) is rapidly evolving our technology landscape. For enterprises exploring generative AI use cases, experimenting with fine-tuned large language models (LLMs) can have a profound impact on results.
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