Covers: implementation of Sequence models
Estimated time needed to finish: 30 minutes
Questions this item addresses:
  • Is any one of those three superior than the other two?
How to use this item?

TLDR: Jump to section 5 & 6

Author(s) / creator(s) / reference(s)
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, Yoshua Bengio
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Sequence Models: Rnn, Lstm & Gru

Total time needed: ~2 hours
Learning the variations of RNNs and the differences between them in one short list
Potential Use Cases
from simple classification problems on sequential input to speech recognition, language modeling, translation, image captioning etc.
Who is This For ?
INTERMEDIATElearners for deep learning in natural language processing
Click on each of the following annotated items to see details.
ARTICLE 1. Clearly explained with illustration: LSTM networks
  • What is different of LSTM from RNN, and why do we need LSTM when we have RNN?
20 minutes
REPO 2. Character-level RNN code implementation with pytorch
  • How do I implement RNN in pytorch and what can be one of the use cases?
20 minutes
ARTICLE 3. Illustrated Guide to LSTMs and GRUs
  • What is GRU?
30 minutes
REPO 4. LSTM for POS tagging using Pytorch
  • How do I implement LSTM in pytorch?
20 minutes
ARTICLE 5. Which is the best, GRU or LSTM or simple RNN?
  • Is any one of those three superior than the other two?
30 minutes

Concepts Covered

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