Deep Learning in Science
Deep Learning in Science
复制标题
科学中的深度学习
DOI：
10.1017/9781108955652
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发表时间：
2021
影响因子：
5
通讯作者：
Pierre Baldi
中科院分区：
文献类型：
--
作者：
Pierre Baldi
这是深度学习理论的第一个严格的、独立的处理。从理论的基础开始并建立它，这对于任何对人工智能和深度学习感兴趣的科学家，教师和学生来说都是必不可少的阅读。它提供了如何思考科学问题的指导，并引导读者了解该领域的历史及其与神经科学的基本联系。作者讨论了自然科学中美丽问题的许多应用，在物理学，化学和生物医学。例如，在实验物理学中寻找奇异粒子和暗物质，在化学中预测分子性质和反应结果，在自然科学中预测蛋白质结构和生物医学图像的诊断分析。课文附有一整套不同难度的练习，鼓励开箱即用的思维。
This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.