Applied Deep Learning – Class 42 | Target Contextual Embeddings (Q,KV) скачать в хорошем качестве

Applied Deep Learning – Class 42 | Target Contextual Embeddings (Q,KV)

In this session of Applied Deep Learning, we complete our exploration of Self-Attention by introducing and explaining Query, Key, and Value vectors — the core components that make attention work. This lecture is theory-only, focused on intuition and understanding how self-attention actually computes meaningful representations for sequence data. 📚 In this lecture, we cover: 🔹 What Query, Key, and Value vectors are Learn how each word in a sentence generates three distinct vectors — Query (Q), Key (K), and Value (V) — that help the model decide what to focus on. 🔹 Example with a sentence We walk through a sentence example and show how Q, K, and V are assigned to each word. 🔹 Parallel computation using matrices See how multiple Q, K, V vectors from an entire sentence are used together as matrices to compute attention scores in parallel — not one word at a time. 🔹 Random initialization and learning Understand that Q, K, and V matrices start with random values, and during training: ✔ Predictions are made ✔ A loss is calculated ✔ Backpropagation updates these vectors This learning process allows the network to refine attention patterns automatically. 🔹 Why this matters This mechanism is what lets self-attention models generate contextualized embeddings, where each word’s representation adapts to the full sentence rather than being fixed. 📂 Notebook Link: https://github.com/GenEd-Tech/Applied... 👍 Like, Share & Subscribe for more AI, NLP & Deep Learning content 💬 Comment if you want the next session on Multi-Head Attention and full Transformer blocks #DeepLearning #SelfAttention #QueryKeyValue #ContextualEmbeddings #Transformer #NLP #MachineLearning #AI #AppliedDeepLearning