What are gradient descent algorithms?
Gradient descent algorithms are optimization techniques used to minimize a function by iteratively moving towards the steepest descent.
How do they work in machine learning?
In machine learning, gradient descent helps adjust model parameters to minimize the loss function, improving predictions.
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 use cases.
Can you explain the difference between stochastic and batch gradient descent?
Sure! Stochastic gradient descent updates parameters using one data point at a time, while batch gradient descent uses the entire dataset.
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