🌟 What does “learned weight matrix” mean?
In machine learning (including Transformers), a weight matrix is like a table of numbers that the model uses to transform input data.
✅ “Learned” means:
- The model doesn’t start with fixed numbers.
- Instead, during training, it adjusts these numbers again and again to improve performance.
🔧 Example in the Transformer
When creating the Query, Key, and Value vectors, we multiply the word embeddings by weight matrices:
Q=EmbeddingĂ—WQ, K=EmbeddingĂ—WK, V=EmbeddingĂ—WV
Here:
- WQ, WK, WV are the learned weight matrices.
- They start with random numbers.
- As the model trains on data, it adjusts these numbers (using optimization algorithms like gradient descent) to reduce error and improve accuracy.
đź’ˇ Simple analogy
Think of the weight matrix like a recipe:
- Initially, you guess ingredient amounts (random weights).
- You taste the dish (check loss/error).
- You adjust the recipe (update weights).
- Over time, you learn the best combination for great results.
🚀 Why is it important?
Without learning the weight matrix:
- The model would just apply fixed, useless transformations.
- With learning, the model adapts itself to the data, finding the best patterns to make good predictions.
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