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Hello and welcome to this video on gradient descent and its types.

Gradient descent is an optimization algorithm used to find the minimum of a function. It is widely used in machine learning to update the parameters of a model in order to minimize the cost function.

There are several types of gradient descent, including batch gradient descent, stochastic gradient descent, and mini-batch gradient descent.

Batch gradient descent involves calculating the gradient of the cost function with respect to the model parameters for the entire training dataset at each iteration. This can be computationally expensive and slow, but it guarantees convergence to the global minimum.

Stochastic gradient descent involves calculating the gradient of the cost function with respect to the model parameters for a single training example at each iteration. This makes the algorithm much faster, but it may not always converge to the global minimum and can sometimes oscillate around it.

Mini-batch gradient descent is a combination of the two, where the gradients are calculated for a small batch of training examples at each iteration. This strikes a balance between the computational efficiency of stochastic gradient descent and the convergence guarantees of batch gradient descent.

In summary, gradient descent is an important optimization algorithm in machine learning and there are several types to choose from depending on the needs of your specific application.

Thank you for watching and I hope you have a better understanding of gradient descent and its types.

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