Course info

COURSE OUTCOMES

Upon the successful completion of the course, the student will be able to

Theroy Component

CO1: Relate neural network architectures and its model parameters.

CO2: Design and evaluate CNN and RNN models for various applications.

CO3: Implement methods of optimization & regularization for Deep Forward Neural Networks.

CO4: Demonstrate the understanding of auto-encoders, GAN and GCN models.

CO5: Construct deep model capabilities to solve real-world problems.

Practical Component

CO1: implement CNN and RNN models for image and text classification.

CO2: develop encoder and transformer models to solve real world problems.