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SuperDataScience Founder Kirill Eremenko returns to the podcast to speak with Jon Krohn about transformer architecture, why they are a new frontier for generative AI and gives a sneak peek into his new course on large language models (LLMs) If you’re interested in applying LLMs to your business portfolio, you’ll want to pay close attention to this episode! Watch the full episode here: https://bit.ly/sds759 Thank you to Ready Tensor, Inc., Oracle NetSuite, and Hewlett Packard Enterprise and Intel
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A simple overview of the Transformer Architecture Transformers process inputs step by step first, text (or tokens) is converted into embeddings and enriched with positional encodings to preserve the order of words In the encoder, layers of self-attention (multi-headed) and linear/feed-forward networks transform these embeddings into contextual representations, where each token "looks at" or attends to others to capture meaning The decoder then takes these encoder outputs along with its own self-
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