参考:Yuhhw:Deep learning and process understanding for data-driven Earth system science 笔记(自学用) 摘要 机器学习方法越来越多地用于从不断增加的地理空间数据流中提取模式和见解,当系统行为受空间或时间环境影响时,当前的方法可能不是最优的。与其修正传统的机器学习方法,本文认为这些场景信息应该用作深度学习...
Deep learning and process understanding for data-driven Earth system science Markus Reichstein et al 2019 in Nature 这是一篇综述性文章,介绍了机器学习如何在地学中的应用与挑战。地球系统科学进入了大数据时代。地球系统数据就是典型的大数据,具备大数据四大特征:volume, velocity, variety and veracity(体积,速度,...
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rath
Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today’s machine intelligence. Numerous success stories have rapidly spread all over science, industry and society, but its limitations have only recently com
Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N (2019) Deep learning and process understanding for data-driven earth system science. Nature 566(7743):195–204. https://doi.org/10.1038/s41586-019-0912-1 Article Google Scholar Song X, Zhang G, Liu F, Li D,...
1. The rise of deep learning 2. Back to fundamentals 3. Geometrical interpretation of DL 4. Relevance of Occam's razor and equifinality? 5. Fundamental differences from other ML methods 6. How to introduce order, time-dependency, and memory 7. ML versus process-based modelling – an experim...
Students agreed that the project encouraged learning for understanding, engagement, confidence and self-efficacy, and personal growth. To address the problems of obtaining insufficient feedback during the writing of a scientific manuscript based on the project, a peer assessment component was introduced ...
1. AI hardware and software Unlike general-purpose computer code (like the software you might use every day when you run a word processor or web browser), deep learning algorithms are usually built in different ways that compose a small number of linear algebra operations: matrix multiplication,...
. With machine learning, you’d need to tell the tool which characteristics can be differentiated. In this case, it would be the leaf type. But with deep learning, the features are already singled out byneural networks. This is all done through an automated and unsupervised learning process....
Deep learning is a complex machine learning algorithm that involves learning inherent rules and representation levels of sample data through large neural networks with multiple layers. It is popular for its automatic feature extraction capabilities and is applied in various areas such as CNN, LSTM, RN...