Titlebook: Deep Learning for Hydrometeorology and Environmental Science; Taesam Lee,Vijay P. Singh,Kyung Hwa Cho Book 2021 The Editor(s) (if applicab

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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.

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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.

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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.

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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

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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

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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.

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