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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 ?
People interested in knowing how Deep Learning model training works.
Click on each of the following
to see details.
1. Intuitive understanding of Backward Propagation
What is Forward Propagation?
What is Backward Propagation?
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.
3. Algorithm for backpropagation
Pseudocode for Back Propagation
4. Intuitive understanding of Total differential
Total differential as linear approximation around a point
5. Mathematical definition of Total derivative
Definition of Total derivative
Examples of calculating Total derivative
6. Multi Variable Chain Rule
What is multivariable chain rule
7. Total differential and Chain rule
How is total differential related to total derivative?
8. Forward Propagation in a Deep Network
What does it mean to perform ForwardPropagation in Neural Networks
9. [Long Read] Detailed description of BackPropagation
Detailed description of BackPropagation
Code for BackPropagation
10. [Long Read] Chain rule
Examples for chain rule application
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