▬▬ Resources/Papers ▬▬▬▬▬▬▬
- Colab Notebook: https://colab.research.google.com/dri...
- DDPM: https://arxiv.org/pdf/2006.11239.pdf
- DDPM Improved: https://arxiv.org/pdf/2105.05233.pdf
- Awesome Diffusion Models Github: https://github.com/heejkoo/Awesome-Di...
- Outlier Diffusion Model Video:    • Diffusion Models | Paper Explanation ...  
- Positional Embeddings: https://machinelearningmastery.com/a-...

▬▬ Used Icons ▬▬▬▬▬▬▬▬▬▬
All Icons are from flaticon: https://www.flaticon.com/authors/freepik

▬▬ Used Music ▬▬▬▬▬▬▬▬▬▬▬
Music from Uppbeat (free for Creators!):
https://uppbeat.io/t/prigida
Song: Spooky Loops
License code: QKVNF1BODEDX33HO

▬▬ Timestamps ▬▬▬▬▬▬▬▬▬▬▬
00:00 Introduction
00:30 Generative Deep Learning
02:58 Diffusion Models Papers / Resources
04:06 What are diffusion models?
05:06 How to implement them?
05:29 [CODE] Cars Dataset
06:50 Forward process
10:15 Closed form sampling
12:15 [CODE] Noise Scheduler
16:10 Backward process (U-Net)
19:32 Timestep Embedding
20:52 [CODE] U-Net
25:35 Loss
26:28 [CODE] Loss
28:53 Training and Results
30:05 Final remarks

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