11 — Mean Square Error Gradient Descent
In statistics, the mean squared error (MSE)[1][2] or mean squared deviation (MSD) of an estimator (of a procedure for estimating an unobserved quantity) measures the average of the squares of the errors — that is, the average squared difference between the estimated values and the actual value. MSE is a risk function, corresponding to the expected value of the squared error loss. The fact that MSE is almost always strictly positive (and not zero) is because of randomness or because the estimator does not account for information that could produce a more accurate estimate.
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English:
हिंदी (Hindi)
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Code:
Medium: https://kmeeraj.medium.com/11-mean-square-error-gradient-descent-ae9bdb33548f
github : https://github.com/kmeeraj/machinelearning/tree/develop
Github Demo : https://github.com/kmeeraj/machinelearning/blob/develop/algorithms/Mean%20Square%20Error/Mean%20Square%20Error%20Gradient%20Descent.ipynb
colab: https://colab.research.google.com/gist/kmeeraj/64be04af2bec608270aa3d57ca018b08/11-mean-square-error-gradient-descent.ipynb
Gist: https://gist.github.com/kmeeraj/64be04af2bec608270aa3d57ca018b08
Mean squared error: https://en.wikipedia.org/wiki/Mean_squared_error
Reference : https://towardsdatascience.com/gradient-descent-from-scratch-e8b75fa986cc
Stochastic Gradient Descent: https://towardsdatascience.com/stochastic-gradient-descent-clearly-explained-53d239905d31
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