ARTICLEUnrolling of computational network

Covers: theory of Unfolded Computational Graph
Questions this item adddesses:
  • Unroll RNN computation graph?
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Author(s) / creator(s) / reference(s)
Jason Brownlee
Shortlist
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Recurrent Neural Networks

Hardik SahiTotal time needed: ~30 minutes
Learning Objectives
Understand what is a Recurrent Neural Network
Potential Use Cases
Mathematical foundations behind Deep Learning
Target Audience
BEGINNERPeople interested in knowing how to use deep learning for modeling sequences.
Go through the following annotated items in order:
BOOK_CHAPTER 1. What is a Recurrent Neural Network
  • What is a Recurrent Neural Network?
5 minutes
VIDEO 2. Motivation for RNN
  • What is the motivation behind using RNN?
10 minutes
BOOK_CHAPTER 3. Parameter sharing in RNN
  • How does Parameter Sharing help in training RNN?
10 minutes
ARTICLE 4. Parameter sharing versus Padding
  • How does Parameter Sharing help in training RNN?
5 minutes
ARTICLE 5. Unrolling of computational network
  • Unroll RNN computation graph?
10 minutes
ARTICLE 6. Unfolding Computational Graphs
  • Advantages of unfolding the graph?
20 minutes

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