What is learned weight matrix ?

🌟 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=Embedding×W^Q, K=Embedding×W^K, V=Embedding×W^V

Here:

• W^Q, W^K, W^V 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.

💡 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.