Syllabus
| Topic |
| Introduction & Preliminaries |
| Linear Neural Networks , Hebbian Network |
| Deep Neural Networks (Back Propagation , XOR Problem, Multi Layer Perceptron) |
| Deep Neural Networks II ( Outputs and Loss Functions, Activation Functions) |
| Regularization ( Early Stopping, Ensemble Methods, Dropout, Data Augmentation) |
| Optimization (Steepest Descent, Newton's Method ,Conjugate Graidient) |
| Convolutional Neural Networks |
| Reccurent Neural Networks |
| Hopfield Network , Boltzmann Machine |
| Applications |
| Review |