What is Gradient Descent?
Gradient Descent is an optimization algorithm commonly used in machine learning and deep learning for minimizing a function by iteratively moving towards the steepest descent as defined by the negative of the gradient.
How It Works
The basic idea of Gradient Descent is to update the parameters of the model (like weights in a neural network) to reduce the cost function, which measures how well the model is performing. The process involves the following steps:
- Initialize Parameters: Start with random values for the model parameters.
- Compute the Gradient: Calculate the gradient of the cost function with respect to each parameter.
- Update Parameters: Adjust the parameters in the opposite direction of the gradient by a small step size, known as the learning rate.
Types of Gradient Descent
There are several variants of Gradient Descent, including:
- Batch Gradient Descent: Uses the entire dataset to compute the gradient.
- Stochastic Gradient Descent (SGD): Uses only one training example at a time to compute the gradient.
- Mini-Batch Gradient Descent: Combines both approaches by using a small batch of training samples.
Applications
Gradient Descent is widely applied in training various machine learning models, especially neural networks, making it a fundamental technique in the field.