对分子和材料进行深度学习
1Department of Chemical Engineering, University of Rochester, Rochester, NY.
概括
这本教科书为化学和材料科学提供了深度学习 (DL) 的实用指南. 它涵盖了基本的DL概念及其对分子数据的独特应用.
科学领域:
- 化学和材料科学 化学和材料科学
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 深度学习 (DL) 在科学研究中越来越重要.
- 现有的DL资源缺乏对化学和材料科学应用的关注.
- 分子数据的独特挑战需要专门的DL方法.
研究的目的:
- 为化学和材料科学提供深度学习的系统介绍.
- 弥合目前这些领域的DL教育教材的差距.
- 为研究人员提供知识,以便将DL应用于分子数据.
主要方法:
- 涵盖了DL的基本数学.
- 解释基本的机器学习概念.
- 详细介绍了常见的神经网络架构.
- 提供实施的实际指导.
主要成果:
- 一个全面的概述DL的原则和技术.
- 在化学和材料科学中,DL应用的具体例子.
- 为该领域的从业者提供基础知识.
结论:
- 这本教科书是科学家进入DL的关键资源.
- 它解决了研究人员与分子数据工作的特定需求.
- "活文档"方法确保内容保持最新.
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