CORE LLM

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A hidden state is the output representation of the input tokens after they have been processed by a layer
A hidden state is the output representation of the input tokens after they have been processed by a layer
In a Transformer model, a hidden state is the output representation of the input tokens after they have been processed by a layer. Unlike in Recurrent Neural Networks (RNNs), where a hidden state carries a sequential memory, each hidden state in a Transformer is a vector that represents the comb...
GD
·docs.google.com·
A hidden state is the output representation of the input tokens after they have been processed by a layer
@ 3 DC - How Transformers Work: A Detailed Exploration of Transformer Architecture
@ 3 DC - How Transformers Work: A Detailed Exploration of Transformer Architecture

INCLUDES: The architecture of Transformers: self-attention, encoder–decoder design, positional encoding, and multi-head attention KEY CONCEPTS: Attention mechanism, embeddings, residual connections, normalization, feed-forward layers, and decoder workflows, Tokenization, Tokens

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·datacamp.com·
@ 3 DC - How Transformers Work: A Detailed Exploration of Transformer Architecture
@ 3 DC - What is Attention and Why Do LLMs and Transformers Need It?
@ 3 DC - What is Attention and Why Do LLMs and Transformers Need It?
In this article, we focus on building an intuitive understanding of attention. The attention mechanism was introduced in the “Attention Is All You Need” paper. It is the key element in the transformers architecture that has revolutionized LLMs.
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·datacamp.com·
@ 3 DC - What is Attention and Why Do LLMs and Transformers Need It?
+ AI 1 CHEATSHEET TERMS
+ AI 1 CHEATSHEET TERMS
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y OUTLINE - Data Scientist Roadmap GRAPHIC
y OUTLINE - Data Scientist Roadmap GRAPHIC
Learn to become an AI and Data Scientist using this roadmap. Community driven, articles, resources, guides, interview questions, quizzes for modern AI and Data Science.
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·roadmap.sh·
y OUTLINE - Data Scientist Roadmap GRAPHIC