噱头
发表于 2025-3-26 22:52:55
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彩色的蜡笔
发表于 2025-3-27 03:04:25
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注意
发表于 2025-3-27 05:50:56
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OMIT
发表于 2025-3-27 13:26:40
Deep Learning for Spatial Datasets,Among recent developments of deep learning models, the availability of spatial datasets or images with deep learning is the most significant contribution. In this chapter, a convolutional neural network (CNN) that can analyze the spatial datasets is described. The training procedure of CNN is described with a simple example.
细丝
发表于 2025-3-27 16:35:17
Environmental Applications of Deep Learning,A case study of CNN, a spatial deep learning model, is presented in this chapter for analyzing water quality with remote sensing data. Airborne remote sensing of cyanobacteria with multispectral/hyperspectral sensors is employed for input data and its water quality as output data.
真繁荣
发表于 2025-3-27 18:29:59
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ACME
发表于 2025-3-27 23:50:10
https://doi.org/10.1007/978-3-319-66387-6esented, including the definition and pros and cons of deep learning, followed by the recent applications of deep learning models in hydrological and environmental fields. The structure of the remaining chapters for this book is also explained.
臭了生气
发表于 2025-3-28 04:07:54
https://doi.org/10.1007/978-3-319-66387-6are explained, including the definition of error terms and parameter estimation procedure, since they are similarly used in deep learning models. Also, the basic concept of the time series model is also explained and this part is mainly referred to in the LSTM model chapter.
伪造
发表于 2025-3-28 08:20:14
The Art or Science of Manipulation. The simplest neural network model is introduced and used in the latter part of this book. Then, a full neural network model is described. The parameter estimation procedure employing backward propagation is also explained.
影响深远
发表于 2025-3-28 14:28:18
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