Covers: theory of Symbol to number versus Symbol to symbol differentiation

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

Read the section 6.5.5

Deep learning book

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Contributors

- 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
- Who is This For ?
- INTERMEDIATEPeople interested in knowing how Deep Learning model training works.

Click on each of the following **annotated items** to see details.

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

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