The past two weeks were too busy to keep up with editing and uploading my lecture videos. I know many of you have been waiting to see how I teach auto-encoder and backpropagation by hand ✍️ in Excel! Here it is!
It took me a few years to invent this method to show both forward and backward passes for a non-trivial case of a multi-layer perceptron over a batch of inputs, plus gradient descents over multiple epochs, while being able to hand calculate each step and code in Excel at the same time.
With the release of this video, I am glad I finally get to share this method with the whole world! 🙌 Let me know what you think in the comment!
= Chapters =
• Encoder & Decoder (00:00)
• Equation (10:09)
• 4-2-4 AutoEncoder (16:38)
• 6-4-2-4-6 AutoEncoder (18:39)
• L2 Loss (20:49)
• L2 Loss Gradient (27:31)
• Backpropagation (30:12)
• Implement Backpropagation (39:00)
• Gradient Descent (44:30)
• Summary (51:39)
= Semester Schedule =
1. Image
2. Function
3. Transform
4. Feature
Special Public Lecture: DeepSeek
5. Compare
6. Filter 1D
7. Filter 2D
8. NN
9. MLP
10. AutoEncoder 👈 NEW
11. GAN
12. CNN
13. ResNet
14. YOLO
15. U-Net
16. Camera
17. Epipolar
18. RANSAC
19. Fourier
20. SVM
21. Probability
22. RNN
23. Transformer
24. ViT
25. CLIP
26. Diffusion
27. Latent Diffusion
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Simon Peyton Jones is joining Epic! Simon is a force of nature behind the Haskell programming language and has written amazing papers on type systems, transactional memory, and other fields that will be key in realizing the metaverse.
https://t.co/H1F0TQPwvg
SKA (Square Kilometer Array) Telescope keynote at #SC17. Of the HPC and Data requirements presented, FLOPS would not be hard at 250PF, but bandwidth is immense—200PB/s(!). Even the upcoming Summit has only 1/10th the memory BW. Here again BYTES the challenge in HPC, not FLOPS.