Skip to main content
Back to course
Log in
Get started
Someone wanted to learn this too, so Grasp built them a personal learning path.
Create your own
ML Math and Statistics Refresher
·
Module 1
Linear Algebra and Optimization for ML
1
Linear Model Predictions with Dot Products and Matrix Multiplication
Express linear-model predictions using vector dot products and matrix multiplication.
Express linear-model predictions using vector dot products and matrix multiplication.
2
Computing Gradients with Respect to Vector Parameters
Compute gradients of scalar loss functions with respect to vector-valued model parameters.
Compute gradients of scalar loss functions with respect to vector-valued model parameters.
3
Gradient Descent Updates and the Impact of Learning Rate and Feature Scale
Perform a gradient-descent update and predict how learning rate and feature scale affect convergence.
Perform a gradient-descent update and predict how learning rate and feature scale affect convergence.
4
Interpreting Covariance Matrix Eigenvectors and Eigenvalues
Interpret the eigenvectors and eigenvalues of a covariance matrix as directions and magnitudes of variation.
Interpret the eigenvectors and eigenvalues of a covariance matrix as directions and magnitudes of variation.
Next module isn't ready yet,
Back to course
Previous module
Next module