DEEP LEARNING (2 CREDITS)

Learning Outcomes:

On successful completion of this course, students will be able to: LO1 – explain fundamental concepts of deep learning; LO2 – execute a proper deep learning experimentation workflow; LO3 – analyze architecture of deep learning models; LO4 – Compose a deep learning code in Python programming.

Topics:

1. Introduction to Deep Learning;
2. Practical Aspect in Deep Learning;
3. Basic of Neural Network;
4. Deep Neural Network;
5. Introduction of Convolutional Neural Network;
6. Convolutional Neural Network for Image Classification;
7. Convolutional Neural Network for Object Detection;
8. Convolutional Neural Network for Image Segmentation;
9. Recurrent Neural Network;
10. Long Short Term Memory (LSTM);
11. Autoencoder;
12. Generative Advesarial Network;
13. Review.