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Neural Network Architecture

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hlibbabii edited this page Feb 5, 2019 · 2 revisions

Neural Network Architecture

Here we say about the nn architecture we are using. That we are using an LSTM. Below we list and explain its parameters

** bidir **

** qrnn **

** bs **

** validation_bs **

** bptt **

** em_sz **

** nh **

** nl **

** adam_betas **

** clip **

** reg_fn **

** drop **

  1. Introduction
  2. State of the art
  3. Background information
    1. Brief Introduction to language modeling
    2. Brief Introduction to Neural Networks and Deep Learning
    3. Using Recurrent Neural Networks for Language Modeling
    4. Long Short Term Memory Networks
  4. Vocab modeling
    1. Data Gathering and Tokenization
    2. Reducing Vocabulary Size
    3. Preprocess Configurations
    4. Vocabulary modeling evaluation
  5. Language modeling
    1. Neural network architecture
    2. Neural Network Training
    3. Subword aware accuracy and entropy
    4. Neural Cache
    5. Language modeling evaluation
  6. Building classifiers
    1. Generation of labeled data for classification
    2. Transfer learning
    3. Classification parameter hypersearch
    4. Classifier evaluation
  7. Discussion
  8. Future work
  9. Conclusion
  10. Appendix A. Preprocessing Configurations Examples
  11. Appendix B. Subword Representation Examples
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