Covers: theory of Unfolded Computational Graph

- Unroll RNN computation graph?

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Jason Brownlee

Hardik Sahi**Total 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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