What Gradient Descent Meaning, Applications & Example
An optimization algorithm used to minimize a cost function.
What is Gradient Descent?
Gradient Descent is an optimization algorithm used to minimize the cost or loss function in machine learning and deep learning models. It works by iteratively adjusting the model ’s parameters (e.g., weights in a neural network ) in the direction that reduces the error, using the gradient (or derivative) of the loss function with respect to the parameters.
Types of Gradient Descent
- Batch Gradient Descent: Computes the gradient of the loss function using the entire training dataset. While it provides stable updates, it can be computationally expensive for large datasets.
- Stochastic Gradient Descent (SGD): Computes the gradient using a single training example at a time, leading to faster updates but more noisy and less stable convergence.
- Mini-batch Gradient Descent: A compromise between batch and stochastic, it computes the gradient using a subset (mini-batch ) of the training data, balancing speed and stability.
Applications of Gradient Descent
- Machine Learning: Used in algorithms like linear regression, logistic regression , and support vector machines to minimize the cost function and train models.
- Neural Networks: Powers the training of deep learning models, where gradient descent updates the weights of the network to minimize the loss function across multiple layers.
- Optimization Problems: Solves complex optimization problems in various fields, including finance, engineering, and robotics.
Example of Gradient Descent
An example of Gradient Descent is in training a linear regression model. The algorithm iteratively adjusts the model’s parameters (slope and intercept) to minimize the difference between the predicted and actual values, eventually finding the line that best fits the data points.