There are many DL-Deep learning models I want to ask which model is more significant and easy to implement for researchers who are new to Deep learning. Please give your suggestion, thanks in advance.
- Convolutional Neural Networks (CNNs)
- Long Short Term Memory Networks (LSTMs)
- Recurrent Neural Networks (RNNs)
- Generative Adversarial Networks (GANs)
- Radial Basis Function Networks (RBFNs)
- Multilayer Perceptrons (MLPs)
- Self Organizing Maps (SOMs)
Hello,
It depends on data characteristics i.e., availability, complexity and drift.
Please see flowchart of model selection in Figure 3 from this paper. Also these previously mentioned concept are defined there.
https://www.researchgate.net/publication/359578507_A_Systematic_Guide_for_Predicting_Remaining_Useful_Life_with_Machine_Learning
Muhammad Nasar Ahmad
I suggest reading these articles:
1. https://www.mdpi.com/2072-4292/13/12/2257/pdf
2. https://www.mdpi.com/2071-1050/13/22/12678/pdf
Please consider these links
Article Urban flood modeling using deep-learning approaches in Seoul...
Article Urban land-use land-cover extraction for catchment modelling...
Good luck
Shima Shafiee Qamar Ul Islam Xie-Yuan Xie Berghout Tarek
thank you all for your valuable answers.
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