What is gradient descent?

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🔹 Concept

• Imagine you’re standing on top of a hill in the dark and want to reach the lowest point in the valley.

• You can’t see far, so you take steps downhill in the direction of the steepest slope.

• Each step gets you closer to the bottom.

• That’s exactly what gradient descent does: it adjusts the model’s parameters step by step to minimize errors.

🔹 How It Works

1. Initialize Parameters: Start with random values for the model’s weights.

2. Compute Gradient: Calculate the gradient (slope) of the loss function with respect to the parameters. This tells us the direction of steepest ascent.

3. Update Parameters: Move parameters in the opposite direction of the gradient (steepest descent).

   • Formula:

     $$\theta = \theta - \alpha \cdot \nabla L(\theta)$$

     where:

     • $\theta$ = model parameters

     • $\alpha$ = learning rate (step size)

     • $\nabla L(\theta)$ = gradient of the loss function

4. Repeat: Continue until the loss function converges (reaches minimum or close enough).

🔹 Types of Gradient Descent

• Batch Gradient Descent: Uses the entire dataset to compute the gradient each step (accurate but slow).

• Stochastic Gradient Descent (SGD): Uses one sample at a time (fast but noisy).

• Mini-Batch Gradient Descent: Uses small batches of data (balances speed and accuracy).

🔹 Key Considerations

• Learning Rate: Too small = very slow convergence; too large = may overshoot and fail to converge.

• Local Minima: In complex functions, gradient descent may get stuck in a local minimum instead of finding the global minimum.

✅ In short:
Gradient Descent is an iterative optimization method that updates model parameters step by step in the direction of the steepest descent of the error function, helping the model learn and improve predictions.

Read More :

Explain Random Forest.

What are ensemble methods? Explain bagging and boosting.

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