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What is the computational complexity of gradient descent?
But according to the Machine Learning course by Stanford University, the complexity of gradient descent is O(kn2), so when n is very large is recommended to use gradient descent instead of the closed form of linear regression.
What is the K fold cross-validation training technique?
Cross-validation is a resampling procedure used to evaluate machine learning models on a limited data sample. The procedure has a single parameter called k that refers to the number of groups that a given data sample is to be split into.
What is the computational cost of gradient descent?
The computational cost of gradient descent depends on the number of iterations it takes to converge. But according to the Machine Learning course by Stanford University, the complexity of gradient descent is O ( k n 2), so when n is very large is recommended to use gradient descent instead of the closed form of linear regression.
How is gradient descent used in linear regression?
Todays blog is all about gradient descent, explained through the example of linear regression. Gradient descent is used to find the best fit for a straight line through a cloud of data points. Therefore, it minimizes a cost function.
How long does it take to run a gradient descent function?
So our gradient descent takes around 82 milliseconds for execution (1000 epochs). Here, we plotted the graph for cost_history . As we can see the graph converges at around 400 epochs, so I run the gradient descent function with epochs= 400, and this time it takes around 25.3 milliseconds.
Which is faster, stochastic gradient descent or SGD?
In stochastic Gradient Descent, we use one example or one training sample at each iteration instead of using whole dataset to sum all for every steps SGD is widely used for larger dataset trainings and computationally faster and can be trained in parallel It is similar like SGD, it uses n samples instead of 1 at each iteration.