Gradient Descent In Machine Learning-FAQs
What is the fastest gradient descent algorithm?
The fastest gradient descent algorithm depends on the specific problem being solved. However, some of the most commonly used variants include stochastic gradient descent (SGD), mini-batch gradient descent, and momentum-based gradient descent.
What is the gradient descent algorithm gda
Gradient Descent Algorithm (GDA) is an iterative optimization algorithm used to find the minimum of a function. It works by repeatedly moving in the direction of the negative gradient of the function, which is the direction that leads to the steepest descent.
Why is gradient descent used?
Gradient descent is used to find the minimum of a function. This is useful for many optimization problems, such as training machine learning models and minimizing the cost of a function.
Which is an example of gradient descent algorithm?
An example of a gradient descent algorithm is linear regression. In linear regression, gradient descent is used to find the coefficients of a linear model that best fits a set of data points.
What is the formula of the gradient?
The formula for the gradient of a function f(x) is: where x is a vector of input variables and is the partial derivative of f with respect to
Gradient Descent Algorithm in Machine Learning
Think about how a machine learns from the data in machine learning and deep learning during training. This involves a large amount of data.
Through the lens of this article, we will delve into the intricacies of minimizing the cost function, a pivotal task in training models.
Table of Content
- Gradient Descent in Machine Learning
- Gradient Descent Python Implementation
- How the Gradient Descent Algorithm Works
- Gradient Descent Learning Rate
- Vanishing and Exploding Gradients
- Different Variants of Gradient Descent
- Advantages & Disadvantages of gradient descent
- Gradient Descent In Machine Learning-FAQs