Gradient Descent with Stochastic Gradient Descent

Requirements Clarification & Assessment

1. Objective Understanding:

   • The primary goal is to understand the differences between Gradient Descent (GD) and Stochastic Gradient Descent (SGD).
   • Identify scenarios where one method might be preferable over the other.

2. Contextual Application:

   • Assess the size of the dataset and computational resources available.
   • Consider the precision required in the optimization process.

3. Algorithmic Knowledge:

   • Understand the mathematical foundation of both GD and SGD.
   • Familiarity with concepts such as learning rate, convergence, and optimization landscapes.

4. Performance Metrics:

   • Determine success based on convergence speed, computational efficiency, and accuracy.
   • Evaluate robustness to noisy data and ability to escape local minima.

5. Practical Constraints:

   • Consider hardware limitations, especially memory and processing power.
   • Time constraints for model training and deployment.