Aban 99
| Week | Date | Main Session | Coursera Modules | TA Session |
|---|---|---|---|---|
| 1 | 17 - 23 Aban | Introduction | Study ML Basics if Needed (non-Coursera) C1M1: Introduction to Deep Learning (2h) |
Introduction to Colab, Numpy, Visualization with Matplotlib, other libs Pandas, Scikit-learn |
| 2 | 24 - 30 Aban | Introduction slides (two sessions) |
C1M2: Neural Networks Basics (8h) | Tensorflow architecture slides Discussion about ML Basics, C1M1, First Assignment |
Azar 99
| Week | Date | Main Session | Coursera Modules | TA Session |
|---|---|---|---|---|
| 3 | 1 - 7 Azar | Deep Learning Intuition slides |
C1M3: Shallow Neural Networks (5h) C1M4: Deep Neural Networks (5h) |
Tensors, Basic operations in tensorflow notebook |
| 4 | 8 - 14 Azar | Adversarial Attacks Generative Adversarial Networks (GAN) |
C2M1: Practical Aspects of Deep Learning (8h) C2M2: Optimization Algorithms (5h) |
Tensorflow model design APIs slides |
| 5 | 15 - 21 Azar | Adversarial Attacks Generative Adversarial Networks (GAN) slides (two sessions) |
C2M3: Hyperparameter Tuning, etc. (5h) C3: ML Strategy (5h) |
Tensorflow model training APIs slides |
| 6 | 22 - 28 Azar | Project Proposal Presentation | C4M1: Foundations of Convolutional Neural Networks (6h) C4M2: Deep Convolutional Models: Case Studies (5h) |
Project Proposal Deadline: 21 Azar |
Dey 99
| Week | Date | Main Session | Coursera Modules | TA Session |
|---|---|---|---|---|
| 7 | 29 Azar - 5 Dey | Convolutional Neural Networks slides |
C4M3: Object Detection (4h) C4M4: Special Applications: Face Recognition & Neural Style Transfer (5h) |
CIFAR-10 Image classification example with tensorflow notebook |
| 8 | 6 - 12 Dey | Transfer Learning slides |
C5M1: Recurrent Neural Networks (6h) | Saving, restoring tensorflow models slides |
| 9 | 13 - 19 Dey | Recurrent Neural Networks | C5M2: Natural Language Processing & Word Embeddings (4h) C5M3: Sequence Models & Attention Mechanism (5h) |
TensorBoard |
| 10 | 20 - 26 Dey | Recurrent Neural Networks slides (two sessions) |
– | Introduction to Pytorch slides basics notebook cifar-10 example |
Bahman 99
| Week | Date | Main Session | Coursera Modules | TA Session |
|---|---|---|---|---|
| 11 | 27 Dey - 3 Bahman | Interpretability of Neural Networks slides |
– | Progress Report: Dey 30th |
| 12 | 4 - 10 Bahman | Transformers slides |
– | Pytorch |
| 13 | 11 - 18 Bahman | Deep Reinforcement Learning | – | HW Discussion |
Final project report and presentation: 27 Bahman