Gradient Descent Variants with MSE Loss
Calculus & Optimization DS practice problem on Onlearn.
Difficulty: medium.
Topics: Understanding Implement Gradient Descent Variants with MSE Loss, Mean Squared Error (MSE), Learning Rate Hyperparameter, Batch Size Configuration, Gradient Computation, Iterative Weight Updates, Optimization, Machine Learning Fundamentals, Calculus, Numerical Methods, Statistical Learning, Gradient Descent Algorithms, Loss Function Design, Model Training Strategies, Iterative Optimization, Parameter Estimation Techniques.
In this problem, you need to implement a single function that can perform three variants of gradient descent Stochastic Gradient Descent (SGD), Batch Gradient Descent, and Mini Batch Gradient Descent using Mean Squared Error (MSE) as the loss function. The function will take an additional parameter to specify which variant to use. Note: Do not shuffle the data