Gradient Descent is an algorithm used in machine learning and artificial intelligence to find the best solution (or parameters) for a model. It’s a way to minimize a function, which in the context of machine learning is usually a cost or loss function that measures how wrong our model is in terms of its ability to estimate the relationship between input and output data.

Here’s a simplified explanation:

Imagine you are on a hill and you want to get down to the bottom, but it’s really foggy and you can’t see where you’re going. You can feel the ground under your feet, though, and you know that if you take a step and the ground slopes downwards, you’re going in the right direction.

So, you take a step, see how much lower you are (calculate the slope or gradient), and then take another step in that direction. You keep doing this until you can’t go any lower. That’s basically what Gradient Descent does – it takes steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point, in order to move towards the minimum of the function.

In the context of machine learning, you start with random parameters for your model, calculate the gradient of the loss function at this point, and then adjust the parameters in the direction that decreases the loss function most rapidly. You continue to adjust the parameters in this way, step by step, until you reach a point where the function is as low as it can go (the global minimum), which gives you the best parameters for your model.

The learning rate is an important part of gradient descent, determining how big the steps you take are. If it’s too small, the descent can be very slow. If it’s too large, you might overshoot the minimum and the algorithm may fail to converge, or even diverge.

The main types of gradient descent are batch (uses all training examples in each step), stochastic (uses a single randomly-chosen training example in each step), and mini-batch (uses a small random sample of training examples in each step).

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