Gradient Descent
Gradient Descent is one of the most popular optimization algorithms in machine learning and statistics. It is used to minimize a function by iteratively moving towards the steepest descent direction as defined by the negative of the gradient.
It is commonly used for training machine learning models, especially in deep learning. The basic idea is easy to understand: take steps proportional to the negative of the gradient to find the local minimum of a function.
How it works
- Start with an initial guess.
- Calculate the gradient of the function at the current point.
- Move in the opposite direction of the gradient to reach the minimum.
- Repeat until convergence.
Applications
Gradient Descent is used in various applications, such as:
- Neural Network training
- Linear Regression
- Logistic Regression
- Support Vector Machines