Deep Learning IIT KGP Course through Blog

Topics Covered In This Course:

1. Inroduction and Feature Descriptor 

2. Bayesian Learning 

3. Discriminant Function 

4. Linear Classifier 

5. Support Vector, Linear Machine and Multi class Support Vector Machines

6. Optimisation and Non Linear Function

7. Neural Network and Multi layer Perceptron

8. Back Propagation Learning and Loss function

9. Autoencoder, PCA , Autoencoder training and Autoencoder variants

10. Convolution, Cross Correlation, CNN Architectur, MLP vs CNN, some example of CNN, Transfer Learning

11. Vanishing and Exploding Gradient, Googlenet, Resnet, Optimiser

12. Normalization, Training Trick, Regulization and Early Stopping

13. Face Recognition, Deconvolution Layer and Semantic segmentation

14. Image Denoising, Variational Autoencoder and Generative adverserial network 

15. Assignments