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Kicking off a series of videos where I'll be going through the actual code of many of the papers I've covered over the last few years!

In this video I do a code walkthrough of OpenAI's CLIP model from the "Learning Transferable Visual Models From Natural Language Supervision" paper.

Let me know what you'd like me to cover next!

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✅ GitHub: https://github.com/openai/CLIP
✅ Paper: https://arxiv.org/abs/2103.00020

Learn about Byte-Pair Encoding:
✅ https://en.wikipedia.org/wiki/Byte_pa...
✅ https://leimao.github.io/blog/Byte-Pa...
✅ Video:    • 1 5 Byte Pair Encoding  
✅    • Subword Tokenization: Byte Pair Encoding  

Unicode:
✅ https://dmitripavlutin.com/what-every...
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⌚️ Timetable:
00:00:00 Intro
00:02:00 High level overview: Interacting with CLIP
00:26:11 High level overview: Prompt engineering for ImageNet
00:40:25 Deep dive starts: vocabulary and byte-pair encoding
00:49:00 Vision Transformer & Text Transformer explained
01:02:00 Tokenization walkthrough
01:09:25 Encoding the image
01:15:15 Encoding the text
01:23:15 Learning a linear probe
01:27:00 Tokenization of the (brain emoji)
01:29:56 Outro

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Huge thank you to these AI Epiphany patreons:
Eli Mahler
Kevin Stone
Petar Veličković

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#CLIP #contrastive #codewalkthrough