Steepest Gradient Descent Method
- Login to Download
- 1 Credits
Resource Overview
Detailed Documentation
The Steepest Gradient Descent Method is one of the most fundamental optimization algorithms in machine learning. This algorithm can find the minimum value of a function based on its gradient, meaning it calculates the function's slope and moves in the direction of the steepest descent, adjusting parameters to converge toward the function's minimum. In code implementation, this typically involves iteratively updating parameters using the formula: θ = θ - α * ∇J(θ), where α represents the learning rate and ∇J(θ) is the gradient of the cost function. The algorithm is widely used in neural network training and stands out among various optimization methods for its simplicity and low computational cost, making it applicable to large datasets. Key implementation considerations include choosing an appropriate learning rate and convergence criterion to ensure stable optimization.
- Login to Download
- 1 Credits