Deep Learning Fundamentals

Find implementations at the following hyperlinks.

1. K - Nearest Neighbour with cross validation

2. Backpropagation through Linear Layer derivation

3. Multi-class SVM Backprop derivation

4. Multi-class SVM implementation

5. Softmax Layer and Cross Entropy Loss Backpropagation derivation

6. Linear network with Softmax Layer and Cross Entropy Loss implementation

7. Two Layer Neural Net with ReLU forward and backward pass

8. Convolutional Neural Networks:

   • Convolution Layer Forward Pass Implementation

   • Convolution Layer Backward Pass Implementation

9. Batch Normalization:

   • Forward Pass Implementation

   • Batch Normalization Backpropagation derivation

   • Batch Normalization Backprop implementation

   • Spatial Batch Normalization forward pass for CNNs

   • Spatial Batch Normalization Backward pass for CNNs

10. Layer Normalization:

    • Forward Pass Implementation

    • Layer Normalization Backpropagation derivation

    • Layer Normalization Backprop implementation

11. Group Normalization:

    • Forward Pass Implementation

    • Backprop implementation

12. Inverted Dropout:

    • Forward Pass

    • Backprop Implementation

13. Optimizers:

    • Stochastic Gradient Descent

    • Stochastic Gradient Descent with Momentum

    • RMSProp

    • Adam

14. Saliency Maps

    
    
    

15. Adverserial Examples
    
    
    

16. Network Visualization

    
    
    
    
    
    

17. Style Transfer