Difference between revisions of "Optimization Methods"
| Line 10: | Line 10: | ||
* [[Natural Language Processing (NLP)]] | * [[Natural Language Processing (NLP)]] | ||
* [[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]] | * [[Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)]] | ||
| − | * [[Average-SGD Weight-Dropped LSTM (AWD-LSTM)]] | + | ** [[Average-SGD Weight-Dropped LSTM (AWD-LSTM)]] |
* Gradient [[Boosting]] Algorithms | * Gradient [[Boosting]] Algorithms | ||
Revision as of 07:11, 30 June 2019
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- Natural Language Processing (NLP)
- Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Network (RNN)
- Gradient Boosting Algorithms
Methods:
- Stochastic gradient descent (SGD) (with and without momentum)
- L-BGFS
- Adagrad
- Adadelta
- Root Mean Squared (RMSprop)
- Adam
- Hessian-free (HF)
