Introduction to Gradient Descent
These are some of the solutions that is made simple when working with cloud solutions
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Gradient Descent is an optimization algorithm used for minimizing a function by iteratively moving towards the steepest descent, as defined by the negative of the gradient. This method is widely used in machine learning for finding the optimal parameters for models. By adjusting the parameters in the direction of the negative gradient, Gradient Descent ensures that the loss function is minimized, leading to better model predictions. One of the key equations in Gradient Descent is the parameter update rule, given as:
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Gradient Descent is an optimization algorithm used for minimizing a function by iteratively moving towards the steepest descent, as defined by the negative of the gradient. One of the key equations in Gradient Descent is the parameter update rule:
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θt+1 = θt - α θt)
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Where:
- θ represents the parameters being optimized.
- α is the learning rate, which controls the size of the steps taken.
- &nabla L(θ) is the gradient of the loss function with respect to the parameters.
Alternative rendering
$$ \theta_{t+1} = \theta_t - \alpha \nabla L(\theta_t) $$
Where:
- θ represents the parameters being optimized.
- α is the learning rate, which controls the size of the steps taken.
- ∇L(θ) is the gradient of the loss function with respect to the parameters.
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