Gradient descent algorithm:
Gradient descent is an
iterative optimization algorithm commonly used to train machine learning
models. It works by iteratively adjusting the parameters of the model in the
direction of the negative gradient of the cost function until the minimum of
the cost function is reached.
The cost function is a measure of how well the
model is predicting the data. It is typically calculated by comparing the
model's predictions to the actual values in the training set.
The gradient of the cost function is a vector that
points in the direction of the steepest ascent of the function. By moving in
the opposite direction of the gradient, gradient descent is able to find the
minimum of the cost function.
Here is a simplified overview of the gradient
descent algorithm:
- Initialize the model parameters.
- Calculate the cost function and its gradient.
- Update the model parameters in the direction
of the negative gradient.
- Repeat steps 2 and 3 until the cost function
converges or a maximum number of iterations is reached.
Gradient descent is a powerful algorithm that can
be used to train a wide variety of machine learning models, including linear
regression, logistic regression, and neural networks.
Here are some of the advantages of using gradient
descent in machine learning:
- It is a simple and easy-to-understand
algorithm.
- It is very efficient and can be used to train
large models.
- It is very flexible and can be used to train a
wide variety of machine learning models.
Here are some of the disadvantages of using
gradient descent in machine learning:
- It can only find local minima of the cost
function.
- It can be sensitive to the choice of learning
rate.
- It can converge slowly for some problems.
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