| #task | Section | Subsection | Person in Charge |
|---|---|---|---|
| 1 | Introduction | Differential Calculus | Tử Quân |
| 1 | Rules of Calculus | Tử Quân | |
| 1 | Multivariate Chain Rule | Tử Quân | |
| 1 | Geometry of Gradients and Gradient Descent | Tử Quân | |
| What Autodiff Isn’t | |||
| 2 | Autodiff is not finite differences (numerical differentiation) | An Đông | |
| 2 | Autodiff is not symbolic differentiation | An Đông | |
| 2 | What autodiff is ? | An Đông | |
| 3 | Backpropagation Algorithm | Gia Hinh | |
| 3 | Comparision & Summary | Gia Hinh | |
| 4 | Visualization | Hiếu Bảo | |
| 5 | Exercise | Question 1-4 | Anh Triết |
| 6 | Question 5-8 | Minh Quân |
This task assignment is done by Gia Hinh.
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Automatic Differentiation from D2l https://d2l.ai/chapter_preliminaries/autograd.html
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Automatic Differentiation in Machine Learning: a Survey https://arxiv.org/pdf/1502.05767
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Automatic differentiation, Wikipedia https://en.wikipedia.org/wiki/Automatic_differentiation
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What is Automatic Differentiation?, Youtube https://youtu.be/wG_nF1awSSY?si=Nr_S8hEgc2yDOFGP
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Introduction to Autodifferentiation in Machine Learning https://www.asadraza.net/posts/autodiff/autodiff/
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Computing Gradients with Backpropagation https://www.cs.princeton.edu/courses/archive/fall18/cos324/files/backprop.pdf