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· 분류 : 외국도서 > 과학/수학/생태 > 과학 > 지구과학 > 수문학
· ISBN : 9783030647797
· 쪽수 : 204쪽
· 출판일 : 2022-01-28
목차
Chapter 1 Introduction
1.1 What is deep learning?
1.2 Pros and cons of deep learning
1.3 Recent applications of deep learning in hydrometeorological and environmental studies
1.4 Organization of chapters
1.5 Summary and conclusion
Chapter 2 Mathematical Background
2.1 Linear regression model
2.2 Time series model
2.3 Probability distributions
Chapter 3 Data Preprocessing
3.1 Normalization
3.2 Data splitting for training and testing
Chapter 4 Neural Network
4.1 Terminology in neural network
4.2 Artificial neural network
Chapter 5 . Training a Neural Network
5.1 Initialization
5.2 Gradient descent
5.3 Backpropagation
Chapter 6 . Updating Weights
6.1 Momentum
6.2 Adagrad
6.3 RMSprop
6.4 Adam
6.5 Nadam
6.6 Python coding of updating weights
Chapter 7 . Improving model performance
7.1 Batching and minibatch
7.2 Validation
7.3 Regularization
Chapter 8 Advanced Neural Network Algorithms
8.1 Extreme Learning Machine (ELM)
8.2 Autoencoding
Chapter 9 Deep learning for time series
9.1 Recurrent neural network
9.2 Long Short-Term Memory (LSTM)
9.3 Gated Recurrent Unit (GRU)
Chapter 10 Deep learning for spatial datasets
10.1 Convolutional Neural Network (CNN)
10.2 Backpropagation of CNNChapter 11 Tensorflow and Keras Programming for Deep Learning
11.1 Basic Keras modeling
11.2 Temporal deep learning (LSTM and GRU)
11.3 Spatial deep learning (CNN)
Chapter 12 Hydrometeorological Applications of deep learning
12.1 Stochastic simulation with LSTM
12.2 Forecasting daily temperature with LSTM
Chapter 13 Environmental Applications of deep learning
13.1 Remote sensing of water quality using CNN
