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Erschienen in:
2022 | OriginalPaper | Buchkapitel
4. Deep Learning and Applications
verfasst von : Prof. Wengang Zhang, Assoc. Prof. Yanmei Zhang, Dr. Xin Gu, Dr. Chongzhi Wu, Dr. Liang Han
Erschienen in: Application of Soft Computing, Machine Learning, Deep Learning and Optimizations in Geoengineering and Geoscience
Verlag: Springer Singapore
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Abstract
DL attempts to mimic the human brain, albeit far from matching its ability, enabling systems to cluster data and make predictions with incredible accuracy. It consist of multiple layers of interconnected nodes, each building upon the previous layer to refine and optimize the prediction or categorization. This progression of computations through the network is called forward propagation. The input and output layers of a deep neural network are called visible layers. The input layer is where the deep learning model ingests the data for processing, while the output layer is where the final prediction or classification is made.