Gradient descent algorithm using Python
The Gradient Descent is an optimization algorithm widely used in machine learning, deep learning, and artificial intelligence to minimize a cost (or loss) function. It works by iteratively updating model parameters in the direction of the negative gradient of the cost function, with the goal of finding the optimal parameter values that yield the best model performance
In this video, I walk through the implementation of batch gradient descent for linear regression using an advertising dataset with TV, radio, and newspaper spending as predictors of sales using Python software. I begin by loading and standardizing the data through z-score normalization. Furthermore, I discuss the concepts of initializing parameters, making predictions and calculating the cost function with the mean squared error (MSE). I also show how the algorithm updates the bias and weights iteratively and visualize how the cost decreases over iterations, highlighting the impact of different hyperparameters (learning rates) on the speed and stability of convergence.
After demonstrating the custom gradient descent implementation, I compared its results with the scikit-learn’s built-in linear regression model. I split the dataset into training and testing subsets, fit the regression model, and evaluated its accuracy using the R-squared, MSE and RMSE. This comparison illustrated the effectiveness of the gradient descent approach and reinforced the theoretical concepts with practical validation, showing both the inner workings of optimization from scratch and the efficiency of library-based solutions.