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Can you please explain the gradient descent?

Gradient descent is simply used in machine learning to find the values of a function's parameters (coefficients) that minimize a cost function as far as possible. You start by defining the initial parameter's values and from there gradient descent uses calculus to iteratively adjust the values so they minimize the given cost-function.

Does gradient descent work on big data?

Gradient Descent is the most common optimization algorithm and the foundation of how we train an ML model. But it can be really slow for large datasets. That’s why we use a variant of this algorithm known as Stochastic Gradient Descent to make our model learn a lot faster.

What is gradient descent?

Gradient descent is a first-order iterative optimization algorithm for finding the minimum of a function This seems little complicated, so let’s break it down. The goal of the gradient descent is to minimise a given function which, in our case, is the loss function of the neural network. To achieve this goal, it performs two steps iteratively.


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