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Gradient Descent
Gradient Descent is an iterative optimization algorithm used to find the minimum of a function, often the 'loss function' in machine learning. It adjusts the model's parameters (weights and biases) in the direction opposite to the gradient of the loss function, incrementally reducing the error. The 'learning rate' determines the size of each step.
In plain terms
It's like blindly trying to find the lowest point in a valley by taking small steps downhill, checking the steepness of the ground after each step.
Why it matters
It's the fundamental engine that allows most machine learning models, especially neural networks, to learn and improve their performance.
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