BOOK_CHAPTERIntroduction to recurrent neural network (RNN) language model

Covers: theory of Recurrent Neural network
Estimated time needed: 30 minutes
Questions this item adddesses:
  • What is RNN language model?
How to use this item?

Read chapter 9 section 9.2 and 9.3 . You can use next resource if you like video based learning.

URL: https://web.stanford.edu/~jurafsky/slp3/9.pdf
Author(s) / creator(s) / reference(s)
Daniel Jurafsky
Shortlist
publicShare

Encoder-decoder sequence-to-sequence network models

Sharvari DhoteTotal time needed: ~2 hours
Learning Objectives
Learn basics of sequence-to-sequence model and it's applications.
Potential Use Cases
It's applied to wide range of applications in Natural language processing include machine translation, summarization, question answering and dialogue.
Target Audience
BEGINNERData Scientist new to NLP
Go through the following annotated items in order:
BOOK_CHAPTER 1. Encoder-Decoder network explained
  • What is encoder- decoder network ?
30 minutes
BOOK_CHAPTER 2. Introduction to recurrent neural network (RNN) language model
  • What is RNN language model?
30 minutes
VIDEO 3. Recurrent Neural network language model
  • What is recurrent neural network (RNN) and RNN's as Language Models?
30 minutes
ARTICLE 4. Encoder-decoder model application in machine translation
  • How encoder-decoder is used in neural machine translation?
13 minutes
ARTICLE 5. Implement sequence-to-sequence model in Pytorch
  • How to implement simple sequence to sequence machine translation model?
10 minutes

Concepts Convered