EmbeddingGemma 2: an open, lightweight multimodal embedding model
deepmind.google·6d ago
AI Engineering
Hacker News·Sep 12·5 min read
You Don't Need Matrix Calculus
TL;DR
Matrix calculus, often seen in machine learning discussions, is unnecessary for differentiation of vector and matrix functions. Instead, using ordinary differentiation with explicit indices is simpler and more effective.
✦ Why It Matters
Start using ordinary differentiation with explicit indices to simplify your mathematical computations today.
Key Takeaways
- Use ordinary differentiation instead of matrix calculus for simplicity.
- Write out formulas explicitly to avoid complex rules.
- Focus on understanding basic differentiation techniques.
- Apply this method to neural network functions for efficiency.
How It Works
Instead of using matrix calculus, which can be cumbersome, the article suggests writing out the components of vectors and matrices explicitly. For instance, when dealing with a neuron activation function, you can differentiate by manipulating the indices directly, which is straightforward and avoids the pitfalls of matrix calculus.