a$)$ Analyze the gradient descent algorithm used for training deep learning models and

discuss its convergence properties. Explain how learning rate adaptation techniques can

improve the efficiency and stability of the training process.

b$)$ Formulate the dropout regularization technique for deep neural networks and explain

its effect on reducing overfitting. Analyze the theoretical justification for dropout and

discuss its limitations$.$