Why would one build a transformer to solve linear algebra problems when there is numpy.linalg? Check out the video to find out why this is a cool idea and understand how the transformer works that can solve 9 linear algebra problems (e.g. matrix multiplication, inversion).
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📺 Symbolic Mathematics with transformers: https://youtu.be/l7ofrfmVsd0
📺 Transformer explained: https://youtube.com/playlist?list=PLpZBeKTZRGPNdymdEsSSSod5YQ3Vu0sKY
📺 GPT-3: https://youtu.be/5fqxPOaaqi0
📺 Foundation models: https://youtu.be/4Cxz4rnnZ7Q
📺 Interpolation vs. Generalization in Deep Learning: https://youtu.be/-GH9vW5FWUs
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donor, Dres. Trost GbR, Yannik Schneider
Paper 📜: Charton, François. “Linear algebra with transformers.” arXiv preprint arXiv:2112.01898 (2021). https://arxiv.org/abs/2112.01898
🔗 Openreview discussion between author and reviewers: https://openreview.net/forum?id=L2a_bcarHcF
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Outline:
00:00 Linear algebra with transformers
00:41 Weights & Biases (Sponsor)
02:21 Why throwing transformers at linear algebra is cool.
08:08 How do transformers solve linear algebra?
09:50 Encoding matrices for transformers
11:28 Training data and results
12:43 Generalization!?
16:05 Few-shot learning!?
17:36 AI Coffee Break Quiz call to action
Music 🎵 : Secret Job – Godmode
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