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Letter G
Gradient Descent

Gradient descent is an optimization algorithm used to minimize the loss function of a machine learning model by adjusting its parameters iteratively. It calculates the gradient of the loss function with respect to the model’s parameters and updates them in the direction that reduces the error.

Use Cases

Model Training

Optimizing parameters (weights and biases) of neural networks and other models.

Regression Analysis

Finding the optimal coefficients for linear regression models.

Hyperparameter Tuning

Adjusting learning rates and other parameters to improve model performance.

Importance

Optimization

Efficiently finds the optimal parameters that minimize prediction errors.

Scalability

Scales well with large datasets and complex models.

Versatility

Used in various machine learning algorithms for parameter optimization.

Analogies

Gradient descent is like finding the lowest point in a hilly terrain using steps and slope information. You take steps in the direction of the steepest slope until you reach the lowest valley, representing the minimum of the loss function.

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