Reconstructed Slides: [Full Workshop] Reinforcement Learning, Kernels, Reasoning, Quantization & Agents — Daniel Han

Source Video

(Full Workshop) Reinforcement Learning, Kernels, Reasoning, Quantization & Agents — Daniel Han

Method

This deck is reconstructed from the existing video frame captures by detecting likely slide regions with OpenCV, cropping/upscaling those regions, deduplicating similar crops, and OCRing the cropped slide images locally. It is a cleaner companion to the full-stage frame deck.

Reconstructed Slides

• Source frame: slide-001.jpg
• Crop: full [0, 0, 960, 540] score 160.13

• Source frame: slide-002.jpg
• Crop: full [0, 0, 960, 540] score 176.5

• Source frame: slide-003.jpg
• Crop: full [0, 0, 960, 540] score 176.66

• Source frame: slide-004.jpg
• Crop: full [0, 0, 960, 540] score 166.92

• Source frame: slide-005.jpg
• Crop: full [0, 0, 960, 540] score 175.9

• Source frame: slide-006.jpg
• Crop: full [0, 0, 960, 540] score 167.08

• Source frame: slide-007.jpg
• Crop: full [0, 0, 960, 540] score 163.19

Dense Scene-Detected Slide Candidates

• youtube OkEGJ5G3foU dense slides