What is gradient descent?
Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent.
How does it work in machine learning?
In machine learning, gradient descent helps to minimize the loss function, adjusting model parameters to improve predictions.
What are the different types of gradient descent?
There are three main types: Batch Gradient Descent, Stochastic Gradient Descent, and Mini-batch Gradient Descent.
What are the advantages of using gradient descent?
Gradient descent is efficient for large datasets and can converge to a local minimum quickly, making it widely used.
Are there any drawbacks to gradient descent?
Yes, it can get stuck in local minima and may require careful tuning of the learning rate.
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