Wednesday class: Fundamentals of Gradient-Based Learning

Plan

• Model = architecture + loss + data + optimization
• How does this apply to linear regression?
• Add a bias term to reinforce how gradient descent works.
  • Draw loss as function of both weight and bias.
  • Live-code to add bias (intercept) to Lab 4 code

Resources

Interactive Gradient Descent demo