Hey guys, in this episode I explain most of what I know about Transformers. I talk about the architecture, the attention formula, encoder, decoder, self-supervised learning, positional encoding, tokenization, inductive bias, Vision-Transformers, receptive fields...

It was the most technical episode I've recorded so far, and I hope you like it! By the way, it worth listening to this episode with the Transformers paper.

Paper Transformers: ⁠https://arxiv.org/pdf/1706.03762.pdf⁠
Link of OpenAI explaining Next Token Prediction: https://www.linkedin.com/posts/zainhas_the-most-clearest-and-crisp-explanation-ugcPost-7132561633280692224-63AX?utm_source=share&utm_medium=member_desktop
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