Question

What is the Gradient Descent algorithm and how does it help in model optimization?

Hint:

Gradient Descent works like walking downhill to reach the lowest point — each step reduces the model’s error.

Answer 1

Concept: Gradient Descent is a widely used optimization algorithm in machine learning and deep learning. It helps find the optimal values of model parameters (weights and biases) by minimizing a loss function, thereby improving prediction accuracy. Step 1: {\color{red}What is Gradient Descent?}
Gradient Descent is an iterative optimization method that:

• Calculates the gradient (slope) of the loss function
• Updates parameters in the opposite direction of the gradient

This ensures movement toward the minimum loss.
Step 2: {\color{red}Basic Update Rule}
The parameter update is given by: \[ \theta = \theta - \alpha \cdot \nabla J(\theta) \] where:

• $\theta$ = model parameters
• $\alpha$ = learning rate
• $\nabla J(\theta)$ = gradient of the loss function

Step 3: {\color{red}Role in Model Optimization}
Gradient Descent helps by:

• Reducing prediction errors
• Finding parameter values that minimize loss
• Improving model accuracy over iterations

Step 4: {\color{red}Learning Rate Importance}
The learning rate controls step size:

• Too large → overshooting the minimum
• Too small → slow convergence

Choosing the right value is critical for optimization. Step 5: {\color{red}Variants of Gradient Descent}

• Batch Gradient Descent — uses entire dataset
• Stochastic Gradient Descent (SGD) — updates per sample
• Mini-batch Gradient Descent — balance of speed and stability