Overview
Curriculum
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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