DEEP LEARNING (2/2 Credits)

Learning Outcomes:

On successful completion of this course, student will be able to: LO1 – Explain the fundamental deep learning theory; LO2 – Execute proper deep learning experimentation workflows; LO3 – Analyze theoretical deep learning models; LO4 – Construct deep learning codes.

Topics:

1. Recurrent Neural Networks – LAB;
2. Unsupervised Learning – LAB;
3. Transformers – LAB;
4. Unsupervised Learning;
5. Word Embeddings – LAB;
6. Neural Network Foundations;
7. Generative Models – LAB;
8. Graph Neural Networks – LAB;
9. Neural Network Foundations – LAB;
10. Self-Supervised Learning – LAB;
11. Autoencoders – LAB;
12. Convolutional Neural Networks;
13. Convolutional Neural Networks – LAB;
14. Graph Neural Networks;
15. Autoencoders;
16. Probabilistic TensorFlow;
17. Self-Supervised Learning;
18. Recurrent Neural Networks;
19. Regression and Classification;
20. Word Embeddings;
21. Transformers;
22. Reinforcement Learning – LAB;
23. Reinforcement Learning;
24. Regression and Classification – LAB;
25. Generative Models.