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Learning Path

LLM Engineering Roadmap

From tokenisation to fine-tuning, deployment and inference optimisation.

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  1. 1

    Transformers and Attention

    Transformer Architecture, Explained by Hand

    42 pages
    IntermediateTransformers

    The transformer block, self-attention and multi-head attention drawn and annotated page by page — the clearest way to internalise the architecture behind modern LLMs.

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  2. 2

    Large Language Models

    LLM Fundamentals: Tokens to Alignment

    50 pages
    IntermediateLarge Language Models

    How large language models really work: tokenisation, pretraining objectives, instruction tuning, RLHF and decoding strategies, explained visually.

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