I recently made some notes while explaining two basic linear algebra ideas used in machine learning:
- Determinant
- Matrix Inverse A determinant tells us two useful things: • Whether a matrix can be inverted • How a matrix transformation changes area For a 2×2 matrix | a b | | c d | The determinant is: det(A) = ad − bc Example: A = [1 2 3 4] (1×4) − (2×3) = −2 Another important case is when: det(A) = 0 This means the matrix collapses space into a line and cannot be inverted . These are called singular matrices . I also explain the matrix inverse , which is similar to division with numbers. If A⁻¹ is the inverse of A: A × A⁻¹ = I where I is the identity matrix . I attached the visual notes I used while explaining this. If you’re learning ML or NumPy, these concepts show up a lot in optimization, PCA, and other algorithms. https://preview.redd.it/3tcqps3ckzpg1.jpg?width=1080&format=pjpg&auto=webp&s=5f15e65e3b427b9409213adc02e949c885a66fe5 submitted by /u/SilverConsistent9222
Originally posted by u/SilverConsistent9222 on r/ArtificialInteligence
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