Hardik Sahi**Total time needed: **~2 hours

- Learning Objectives
- This lst helps you understand about computational graphs and how they are used in Deep Learning
- Potential Use Cases
- Mathematical foundations behind Deep Learning
- Target Audience
- INTERMEDIATEPeople interested in knowing how Deep Learning model training works.

Go through the following **annotated items** *in order*:

BOOK_CHAPTER 1. Intuitive understanding of Computational Graph

- Explains what is a computational graph

10 minutes

ARTICLE 2. Computational Graph in Deep learning

- Example of using Computational Graph for BackPropagation

15 minutes

BOOK_CHAPTER 3. Subroutines for nodes in a computational graph

- What are the subroutines to be followed in a computational graph?

20 minutes

ARTICLE 4. Symbol to symbol and Symbol to number derivatives

- Intuitive understanding of Symbol to symbol and Symbol to number derivatives

10 minutes

OTHER 5. [Long read] Detailed explanation of Computation Graph and how to apply BackPropagation over it.

- What is computation graph?
- How to apply backprop?

60 minutes

Previewing stream ** Math and Foundations**

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