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gradient descent explained

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Gradient Descent Explained

#artificialintelligenceDec-25-2021, 07:05:06 GMT

Starting at the top of the mountain, we will take baby steps downhill in the direction where the gradient is negative. After that, we recalculate the negative gradient and take another step in a specified direction. We continue this process until we get to the bottom or local minimum. The size of each step is called a learning rate. If you have a high learning rate it means your descent will be much faster, but you will risk overshooting the local minima.

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Technology: Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Gradient Descent (0.45)

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