Optimization with Gradient Descent
Explore how to implement gradient descent in Python to optimize a simple predictive model for restaurant tips. Understand the gradient calculation, stopping conditions, learning rate, and types of gradient descent including batch, stochastic, and mini-batch methods. This lesson helps you apply gradient descent for effective parameter tuning and error reduction in predictive analysis.
In the previous lesson, we looked at the intuition behind the gradient descent algorithm and the update equation. In this lesson, we are going to implement it in Python. We are going to predict the tips paid by a customer at a restaurant. We will choose the best model using gradient descent.
Minimization with Gradient Descent
Recall that the gradient descent algorithm is
- Start with a random initial value of .
- Compute