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Understanding Backpropagation

Contributors
Total time needed: ~4 hours
Objectives
This list helps you understand how backpropagation algorithm is used to calculate gradient of the loss function with respect to parameters of the model
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.
Resources5/11
VIDEO 1. Intuitive understanding of Backward Propagation
  • What is Forward Propagation?
  • What is Backward Propagation?
10 minutes
ARTICLE 2. Backpropagation as Reverse Mode Differentiation
  • What is Forward Mode Differentiation?
  • What is Reverse Mode Differentiation and how is BackPropagation a special case of it?
  • Easy to understand example differentiating the above two.
20 minutes
ARTICLE 3. Algorithm for backpropagation
  • Pseudocode for Back Propagation
20 minutes
VIDEO 4. Intuitive understanding of Total differential
  • Total differential as linear approximation around a point
10 minutes
ARTICLE 5. Mathematical definition of Total derivative
  • Definition of Total derivative
  • Examples of calculating Total derivative
10 minutes
ARTICLE 6. Multi Variable Chain Rule
  • What is multivariable chain rule
10 minutes
ARTICLE 7. Total differential and Chain rule
  • How is total differential related to total derivative?
20 minutes
VIDEO 8. Forward Propagation in a Deep Network
  • What does it mean to perform ForwardPropagation in Neural Networks
10 minutes
ARTICLE 9. [Long Read] Detailed description of BackPropagation
  • Detailed description of BackPropagation
  • Code for BackPropagation
60 minutes
ARTICLE 10. [Long Read] Chain rule
  • Examples for chain rule application
30 minutes
ARTICLE 11. [Long Read] The Matrix Calculus You Need For Deep Learning
  • Detailed understanding of how to apply Matrix calculus to calculate gradient of loss function using chain rule
60 minutes

Concepts Covered

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