Applied Deep Learning – Class 41 | Parallel Contextual Embeddings

Описание: In this session of Applied Deep Learning, we continue our exploration of Self-Attention with a focus on how contextual embeddings for an entire sentence are computed in parallel using matrices.

This lecture bridges intuition with practical understanding, showing how self-attention scales from a single word to all words at once.

📚 In this lecture, we cover:
🔹 Parallel Contextual Embeddings

We explain how self-attention allows us to generate contextual embeddings for every word in a sentence simultaneously, not one word at a time.

🔹 Matrix-Based Computation

We compute attention scores as matrix products

We apply softmax on the score matrix

We multiply scores with value vectors to get contextualized embeddings for all words together

🔹 Example Walkthrough

Using an example sentence, we visually demonstrate how self-attention matrices are built and applied, helping you understand how the transformer processes entire sequences at once.

🔹 Why This Matters

Parallel computation in self-attention enables:
✔ Efficient sequence processing
✔ Better understanding of global context
✔ Foundation for Transformer models

📂 Notebook Link:
https://github.com/GenEd-Tech/Applied...

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#DeepLearning #SelfAttention #ContextualEmbeddings #Transformer #NLP #MachineLearning #AI #AppliedDeepLearning