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Lectures

1. Introduction to Deep Learning
2. Introduction to Machine Learning and Deep Learning
3. Introduction to Neural Networks
4. Lecture Notes on Neural Networks
5. Neural Networks and Deep Learning
6. Training Neural Networks and Backpropagation
7. Neural Networks, Gradient Descent, and Advanced Loss Functions
8. Deep Learning Lecture Notes - Optimization Techniques
9. Advanced Optimization Techniques in Deep Learning
10. Convolutional Neural Networks for Image Understanding
11. Convolutional Neural Networks for Image Processing
12. Convolutional Neural Networks and Modern Architectures
13. Object Detection in Computer Vision
14. Introduction to PyTorch for Deep Learning
15. Lecture Notes on Object Detection and Image Segmentation
16. Lecture Notes on Convolutional Neural Networks
17. Recurrent Neural Networks and Their Applications
18. Lecture Notes on Transformer Models and BERT
19. AI Lab Projects Overview
20. Lecture Notes: AI Fundamentals
21. Graph Neural Networks
22. Graph Neural Networks
23. Lecture Notes: Transformers and Large Language Models
24. Lecture Notes: Transformers and Large Language Models
25. Techniques for Improving System Performance in Deep Learning
26. Techniques for Improving Neural Networks: Regularization
27. Natural Language Processing and Word Embeddings
28. Recurrent Neural Networks and Their Applications
29. Large Language Models: From BERT to ChatGPT
30. Transformer Models and the Attention Mechanism
31. Transformer Models and the Attention Mechanism
32. Large Language Models: From BERT to ChatGPT