DEEP LEARNING (2 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: Explain the fundamental deep learning theory; Execute a proper deep learning experimentation workflow; Analyze a theoretical deep learning model; Compose a deep learning code.

Topics:

1. From Neural Networks to Deep Learning
2. Best Practice to Develop a Deep Learning Model
3. Deep Learning Implementation and Experimentation in Practice
4. Convolutional Neural Networks for Complex Computer Vision Tasks
5. Implementing a Convolutional Neural Network Model
6. Representation Learning with Neural Embedding
7. Advanced Convolutional Neural Networks
8. Deep Learning for Natural Language Processing: Recurrent Neural Network and Transformer
9. Implementing a Deep Learning Model for Text Classification
10. Multimodal Deep Learning
11. The Future of Deep Learning
12. Mathematical Background of Deep Learning
13. Deep Learning for Computer Vision: Convolutional Neural Network