What is gradient descent optimization?
Gradient descent optimization is an iterative algorithm used to minimize a function by adjusting parameters in the direction of the steepest descent.
How does it work in machine learning?
In machine learning, gradient descent helps in minimizing the loss function by updating model parameters based on the gradients.
What are the different types of gradient descent?
The main types are batch gradient descent, stochastic gradient descent, and mini-batch gradient descent, each with its own advantages.
Can you explain the concept of learning rate?
The learning rate determines the size of the steps taken towards the minimum; a small rate may take longer to converge, while a large rate may overshoot.
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