Gradient Descent is an algorithm for finding the minimum of a function. It iteratively calculates partial derivatives (gradients) of the function and descends in steps proportional to those partial derivatives. One major application of Gradient Descent is fitting a parameterized model to a set of data: the function to be minimized is an error function for the model.

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### LBFGS Solver query

Lbfgs definition on the internet: Stands for Limited-memory Broyden–Fletcher–Goldfarb–Shanno. It approximates the second derivative matrix updates with gradient evaluations. Can somebody please ...
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### Why does stochastic gradient descent lead us to a minimum at all?

Why do we think that stochastic gradient descent is going to find a minimum at all? I mean on each iteration SGD moves in the direction that reduces only current batch's error (SGD doesn't care about ...
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### Does gradient descent always find global minimum for specific regression type?

From my understanding, linear regression is used for predicting an output based on an input using a linear equation that is optimally fitted to some input data. We choose the best fitted linear ...
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### Formula of momentum gradient descent optimizer

Learning about the optimizers recently, I was confused about the formula for momentum. I mean, I understood the concept but I came across the following 2 formulas while learning. I see that the left ...
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### How does Gradient Descent work? [duplicate]

I know the calculus and the famous hill and valley analogy (so to say) of gradient descent. However, I find the update rule of the weights and biases quite terrible. Let's say we have a couple of ...