I have looked at gradient decent they say that it is the most important part of machine learning. I am a little confused about this. I understand back propagation and I have looked at linear regression. This is something that I think people are looking for and I have it.
Not sure if this is what you are looking for but…
In machine leaning, gradient descent is an optimization strategy/algorithm.
- The strategy attempts to minimize the error/loss calculated using the error/loss function at the end of the forward pass.
- The back propagation step adjusts the weights of the network to reduce the loss
- Adjustments are made in the direction which will make the loss decrease determined by the derivative of the loss function wrt the input.
- Small adjustments are repeatedly made until the error/loss is at an acceptable level.
This post has some interesting links related to gradient descent.