What is gradient descent?

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Gradient Descent is an optimization algorithm used to train machine learning models, especially neural networks. Its goal is to minimize the loss function (the measure of how wrong a model’s predictions are) by adjusting the model’s parameters (weights and biases).

How it works:

  1. Start with initial weights (random values).

  2. Make a prediction using those weights (forward pass).

  3. Calculate the error (loss function: difference between predicted and actual).

  4. Compute gradients: Find the slope of the loss function with respect to each parameter using calculus (partial derivatives).

  5. Update parameters: Move the weights slightly in the opposite direction of the gradient (downhill), because the gradient points toward the steepest increase.

    • Formula:
      New Weight = Old Weight – (Learning Rate × Gradient).

  6. Repeat until the model converges (loss is minimized).

Key Concepts:

  • Learning Rate (α): Step size for updates.

    • Too large → may overshoot and not converge.

    • Too small → training is very slow.

  • Local vs Global Minimum: Gradient descent may get stuck in local minima (not always the absolute lowest point).

  • Variants:

    • Batch Gradient Descent: Uses the entire dataset per step (slow but stable).

    • Stochastic Gradient Descent (SGD): Updates weights for each data point (fast but noisy).

    • Mini-batch Gradient Descent: Compromise between the two (most widely used).

✅ In short: Gradient descent is like hiking down a hill in the fog — at each step, you look at the slope beneath your feet (gradient) and take a step downward until you reach the bottom (minimum error).

Read More:

What is a neural network?

What is dropout in deep learning?

What is dropout in deep learning?

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