深入学习

IF 18.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Nature Machine Intelligence Pub Date : 2024-05-10 DOI:10.1038/s42256-024-00840-8

Ge Wang

引用次数: 0

摘要

理解深度学习西蒙-普林斯（Simon J. D. Prince）麻省理工学院出版社：2023.544 pp.90.00 美元过去几年，在神经网络深度学习取得突破性进展的推动下，人工智能（AI）领域的发展突飞猛进。这给社会的许多方面带来了变革。然而，人工智能的发展速度凸显了对教科书的需求，这些教科书为年轻的研究人员和专业人士提供了必要的资源，使他们能够掌握深度学习的最新方法和最佳实践，指导他们探索未知领域，充分发挥人工智能的潜力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nature Machine Intelligence Multiple-

CiteScore

36.90

自引率

2.10%

发文量

127

期刊介绍： Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.