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Gradient Descent

Gradient descent is an iterative optimization method that moves parameters in the negative gradient direction to reduce an objective function.

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Meaning

What Is Gradient Descent?

Gradient descent is an iterative optimization method that moves parameters in the negative gradient direction to reduce an objective function.

Gradient descent is an iterative optimization method that moves parameters in the negative gradient direction to reduce an objective function.

Examples

Examples of Gradient Descent

1Update model weights to lower a loss function.
Understand

Formula and Key Points

Formula / rule
θₜ₊₁=θₜ−η∇J(θₜ)
  • Know the definition and standard notation for Gradient Descent.
  • Be able to recognise or compute gradient descent in a small example.
  • Connect the concept to nearby topics in the same subject before using it in larger CSE problems.
CSE Connection

Why This Matters in Computer Science

Used directly in rendering, geometric transforms, optimization and machine-learning models. One of the core optimization methods used to train many machine-learning models.

FAQ

Gradient Descent: Frequently Asked Questions

What is Gradient Descent?

Gradient descent is an iterative optimization method that moves parameters in the negative gradient direction to reduce an objective function.

Why is Gradient Descent useful in computer science?

Used directly in rendering, geometric transforms, optimization and machine-learning models. One of the core optimization methods used to train many machine-learning models.