Contextual Embeddings Explained | How Attention in Transformers Gives Words Meaning

In this video, we break down contextual embeddings and show how the attention mechanism in transformers helps LLMs understand word meaning based on surrounding words like river, steep, and bank.
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You’ll learn how tokenization, embedding, positional encoding, Query-Key-Value matrices, scaled attention, Softmax, and Value aggregation work together to create context-aware representations inside a language model.
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By the end, you’ll see how a word like apple can change meaning depending on the sentence, and why attention is the core idea behind modern LLM understanding

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Видео Contextual Embeddings Explained | How Attention in Transformers Gives Words Meaning канала Katick Sharma