| #!/usr/local/bin/python3 | |
| ''' | |
| Convert a pkl file into json file | |
| ''' | |
| import sys | |
| import os | |
| import _pickle as pickle | |
| import json | |
By Adam Anderson
These notes are mostly just summaries of the provided references. The purpose of this document is to centralize the resources I found useful so they would be easy to find. Most definitions and explanations are paraphrased or quoted directly from the sources.
Question: Why do we use gradient descent in linear regression?
From https://stackoverflow.com/questions/26804656/why-do-we-use-gradient-descent-in-linear-regression
In some machine learning classes I took recently, I've covered gradient descent to find the best fit line for linear regression.
In some statistics classes, I have learnt that we can compute this line using statistic analysis, using the mean and standard deviation - this page covers this approach in detail [http://onlinestatbook.com/2/regression/intro.html]. Why is this seemingly more simple technique not used in machine learning?
My question is, is gradient descent the preferred method for fitting linear models? If so, why? Or did the professor simply use gradient descent in a simpler setting to introduce the class to the technique?
