Gradient Descent: Difference between revisions
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(Created page with "= How it works = After processing all data points, all weights are updated and 1 '''epoch''' is completed. == GD Update Rule == The '''GD update rule''' is used to update the weights after an iteration. === LMS === Least-mean-squared is a GD update rule.") |
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'''Gradient descent (GD)''' is an iterative method to update the weights of a linear regression model to achieve the optimal model. | |||
The downside to GD is that It does not gaurantee the global optimal. To achieve global minimum, tradeoff needs to be made. | |||
The | |||
=== | = Epoch = | ||
1 '''epoch''' is completed when the entire instances in the training sets are processed once to update the weights of the model. | |||
= Hyperparameters = | |||
The '''learning rate''' is a hyperparameter of gradient descent | |||
= LMS = | |||
Least-mean-squared is a GD update rule. | Least-mean-squared is a GD update rule. | ||
= Optimizations = | |||
There are two optimization of gradient descents: | |||
* [[Stochastic Gradient Descent]] | |||
* [[Batch Gradient Descent]] | |||
Revision as of 18:28, 15 April 2024
Gradient descent (GD) is an iterative method to update the weights of a linear regression model to achieve the optimal model.
The downside to GD is that It does not gaurantee the global optimal. To achieve global minimum, tradeoff needs to be made.
Epoch
1 epoch is completed when the entire instances in the training sets are processed once to update the weights of the model.
Hyperparameters
The learning rate is a hyperparameter of gradient descent
LMS
Least-mean-squared is a GD update rule.
Optimizations
There are two optimization of gradient descents:
