Sustainable AI
Raghavendra Selvan
Sold by Rarewaves USA, OSWEGO, IL, U.S.A.
AbeBooks Seller since June 10, 2025
New - Soft cover
Condition: New
Ships within U.S.A.
Quantity: Over 20 available
Add to basketSold by Rarewaves USA, OSWEGO, IL, U.S.A.
AbeBooks Seller since June 10, 2025
Condition: New
Quantity: Over 20 available
Add to basketIn the era of big data and even bigger machine learning models, the environmental footprint of these technologies can no longer be ignored. This much-needed guide confronts the challenge head-on, offering a groundbreaking exploration into making deep learning (DL) both efficient and accessible. Author Raghavendra Selvan exposes the high costs-both environmental and economic-of traditional DL methods and presents practical solutions that pave the way for a more sustainable AI.This essential read is for anyone in the machine learning field, from the academic researcher to the industry practitioner, who wants to make a meaningful impact on both their work and the world. This book enables readers to be agents of change toward a more sustainable and inclusive technological future. In this book, you will:Learn strategies to significantly reduce the energy consumption, carbon footprint, and hardware demands of DL modelsExamine ways to break down barriers and foster a more inclusive future in AI developmentExplore strategies for cutting costs and minimizing ecological impactLearn how to balance performance with efficiency in model developmentGain proficiency in cutting-edge tools that enhance the sustainability of your AI projects.
Seller Inventory # LU-9781098155513
In the era of big data and even bigger machine learning models powering the current generative AI revolution, the environmental footprint of these developments can no longer be ignored. This much-needed guide confronts the challenge head-on, offering a groundbreaking exploration into making deep learning (DL) both efficient and accessible. Author Raghavendra Selvan exposes the high costs—both environmental and economic—of traditional DL methods and presents practical solutions that pave the way for a more sustainable AI.
This essential read is for anyone in the machine learning field, from the academic researcher to the industry practitioner, who wants to make a meaningful impact on both their work and the world. This book enables readers to be agents of change toward a more sustainable and inclusive technological future.
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