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    HomeLarge Language ModelsTransformers and Attention Mechanisms

    Transformers and Attention Mechanisms

    A course by
    Data Science Dojo
    November, 2024 5 lessons English

    In this module, you will explore key concepts of the transformer architecture, embeddings, attention mechanisms, and tokenization. You’ll gain a deeper understanding of semantic similarity and how it is calculated using techniques like dot product and cosine similarity. The module also includes hands-on exercises to help you apply the concepts learned to real-world scenarios.

    What you'll learn

    • ⁠Understand the fundamentals of the transformer architecture and how it is used in modern LLMs.
    • Analyze the role of embeddings, attention, and self-attention mechanisms in processing and generating text.
    • Learn tokenization techniques and their importance in preparing text data for transformer models.
    • Evaluate methods for calculating semantic similarity, such as dot product and cosine similarity, in transformer models.

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