標(biāo)題: Titlebook: Deep Learning for Hydrometeorology and Environmental Science; Taesam Lee,Vijay P. Singh,Kyung Hwa Cho Book 2021 The Editor(s) (if applicab [打印本頁] 作者: Extraneous 時(shí)間: 2025-3-21 19:50
書目名稱Deep Learning for Hydrometeorology and Environmental Science影響因子(影響力)
書目名稱Deep Learning for Hydrometeorology and Environmental Science影響因子(影響力)學(xué)科排名
書目名稱Deep Learning for Hydrometeorology and Environmental Science網(wǎng)絡(luò)公開度
書目名稱Deep Learning for Hydrometeorology and Environmental Science網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Deep Learning for Hydrometeorology and Environmental Science被引頻次
書目名稱Deep Learning for Hydrometeorology and Environmental Science被引頻次學(xué)科排名
書目名稱Deep Learning for Hydrometeorology and Environmental Science年度引用
書目名稱Deep Learning for Hydrometeorology and Environmental Science年度引用學(xué)科排名
書目名稱Deep Learning for Hydrometeorology and Environmental Science讀者反饋
書目名稱Deep Learning for Hydrometeorology and Environmental Science讀者反饋學(xué)科排名
作者: Genetics 時(shí)間: 2025-3-21 23:53 作者: 淘氣 時(shí)間: 2025-3-22 02:32 作者: THROB 時(shí)間: 2025-3-22 06:04 作者: 陪審團(tuán)每個(gè)人 時(shí)間: 2025-3-22 09:50
Improving Model Performance, are explained. The basic idea of these two methods is on controlling the dataset, since repeated usage of the same dataset for training and validation might result in overfitting. Furthermore, regularization of the neural network model training by L-norm regularization and dropout of hidden nodes a作者: colostrum 時(shí)間: 2025-3-22 14:38 作者: colostrum 時(shí)間: 2025-3-22 19:00 作者: 按等級(jí) 時(shí)間: 2025-3-22 21:57 作者: 頌揚(yáng)國家 時(shí)間: 2025-3-23 02:09
0921-092X ues and their applications to hydrometeorological and enviro.This book provides a step-by-step methodology and derivation of deep learning algorithms as Long Short-Term Memory (LSTM) and Convolution Neural Network (CNN), especially for estimating parameters, with back-propagation as well as examples作者: white-matter 時(shí)間: 2025-3-23 08:47
Erkki Tomppo,Juha Heikkinen,Nina Vainikainen to improve the speed of convergence and to find the best trajectory to reach the optimum of the employed loss function for a network. In this chapter, those methods for updating weights are explained.作者: Palpable 時(shí)間: 2025-3-23 13:07
Debas Senshaw,Hossana Twinomurinziy easily getting started with Tensorflow. Keras allows employing Tensorflow without losing its flexibility and capability. In the following, two applications (i.e., temporal and spatial deep learning) are presented to illustrate how to use Keras with python.作者: 金盤是高原 時(shí)間: 2025-3-23 17:34 作者: PAN 時(shí)間: 2025-3-23 18:10
Updating Weights, to improve the speed of convergence and to find the best trajectory to reach the optimum of the employed loss function for a network. In this chapter, those methods for updating weights are explained.作者: THROB 時(shí)間: 2025-3-24 02:09
Tensorflow and Keras Programming for Deep Learning,y easily getting started with Tensorflow. Keras allows employing Tensorflow without losing its flexibility and capability. In the following, two applications (i.e., temporal and spatial deep learning) are presented to illustrate how to use Keras with python.作者: Pantry 時(shí)間: 2025-3-24 03:06
0921-092X their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convo978-3-030-64779-7978-3-030-64777-3Series ISSN 0921-092X Series E-ISSN 1872-4663 作者: persistence 時(shí)間: 2025-3-24 06:38 作者: bile648 時(shí)間: 2025-3-24 11:35
Book 2021ence are very rare.. .This book focuses on the explanation of deep learning techniques and their applications to hydrometeorological and environmental studies with real hydrological and environmental data. This book covers the major deep learning algorithms as Long Short-Term Memory (LSTM) and Convo作者: jocular 時(shí)間: 2025-3-24 18:02
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.作者: 神刊 時(shí)間: 2025-3-24 22:02 作者: orient 時(shí)間: 2025-3-25 00:50 作者: START 時(shí)間: 2025-3-25 05:14
Erkki Tomppo,Juha Heikkinen,Nina Vainikainenhe number of weights exponentially grows, especially in a deep learning machine. In recent years, several methods updating weights have been developed to improve the speed of convergence and to find the best trajectory to reach the optimum of the employed loss function for a network. In this chapter作者: troponins 時(shí)間: 2025-3-25 10:45 作者: Conscientious 時(shí)間: 2025-3-25 11:45
Keith Postlethwaite,Nigel Skinners been developed and applied in a number of fields. Recurrent neural network models can allow forecasting future better, and long short-term memory (LSTM) is a breakthrough to overcome the shortages of the previous RNN model. These algorithms are explained in detail in this chapter.作者: Goblet-Cells 時(shí)間: 2025-3-25 18:59
Debas Senshaw,Hossana Twinomurinziy resources (.). It provides multiple levels of abstractions to choose the right one. The high-level Keras API can be used to build and train models by easily getting started with Tensorflow. Keras allows employing Tensorflow without losing its flexibility and capability. In the following, two appli作者: opportune 時(shí)間: 2025-3-25 20:17
Debas Senshaw,Hossana Twinomurinziology, time-series deep learning models are mainly employed. In this chapter, the development procedure of a time series deep learning model for stochastic simulation producing a long sequence that mimics historical series is explained. Furthermore, the case study for daily maximum temperature with 作者: 的’ 時(shí)間: 2025-3-26 01:30
https://doi.org/10.1007/978-3-030-64777-3Hydrology; Meteorology; Artificial neural networks; Climate index; Convolutional neural networks; Lon Sho作者: 抗生素 時(shí)間: 2025-3-26 08:16
978-3-030-64779-7The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: medium 時(shí)間: 2025-3-26 09:26 作者: 寬敞 時(shí)間: 2025-3-26 13:47 作者: ALIAS 時(shí)間: 2025-3-26 17:38 作者: 噱頭 時(shí)間: 2025-3-26 22:52 作者: 彩色的蠟筆 時(shí)間: 2025-3-27 03:04 作者: 注意 時(shí)間: 2025-3-27 05:50 作者: OMIT 時(shí)間: 2025-3-27 13:26
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.作者: 細(xì)絲 時(shí)間: 2025-3-27 16:35
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.作者: 真繁榮 時(shí)間: 2025-3-27 18:29 作者: ACME 時(shí)間: 2025-3-27 23:50
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.作者: 臭了生氣 時(shí)間: 2025-3-28 04:07
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.作者: 偽造 時(shí)間: 2025-3-28 08:20
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.作者: 影響深遠(yuǎn) 時(shí)間: 2025-3-28 14:28 作者: 財(cái)產(chǎn) 時(shí)間: 2025-3-28 18:27 作者: Highbrow 時(shí)間: 2025-3-28 19:44
Taesam Lee,Vijay P. Singh,Kyung Hwa ChoProvides step-by-step tutorials that help the reader to learn complex deep learning algorithms.Gives an explanation of deep learning techniques and their applications to hydrometeorological and enviro作者: 泥瓦匠 時(shí)間: 2025-3-29 00:55 作者: conspicuous 時(shí)間: 2025-3-29 06:57 作者: Congregate 時(shí)間: 2025-3-29 07:41 作者: 錯(cuò) 時(shí)間: 2025-3-29 11:40 作者: pancreas 時(shí)間: 2025-3-29 19:08
Deep Learning for Time Series,s been developed and applied in a number of fields. Recurrent neural network models can allow forecasting future better, and long short-term memory (LSTM) is a breakthrough to overcome the shortages of the previous RNN model. These algorithms are explained in detail in this chapter.
