JonathonCwik

I use password safe: https://pwsafe.org/ with a strong central password and store the safe it in my Dropbox. Even if my cloud was compromised, the safe is highly encrypted.

PasswordSafe's safe is an open source file structure and thus there are many different ways to access it with different features for each. I have PasswordSafe on both my Windows PC and android phone and I'm using PasswordSafe professionally for my organization's passwords and found that there are reliable Mac options so those with Macs can access the safe.

Ah, gotcha! Makes a lot more sense now.

So I'm still wrapping my head around some of the math (I haven't had a math class in a handful of years)...

I get the output of the model (y_model = m*xs[i]+b), it's the y = mx + b where we know x (from the dataset) and have y be a variable.

The error is where I start to lose it, so I get the idea of the first part (ys[i]-y_model). It's basically the difference between the actual y value (from the dataset). I get that we want this number to be as small as possible as the closer to zero it is for the entire dataset that means we get closer to the line going through (or near) all the points and the closest fit will be when this total_error is nearest to zero.

What I don't get is the squaring of the difference. Is it just to make the difference a larger number so that it's a little more normalized? How do you get to the conclusion that it needs to be normalized? Same thing with the learning rate? I believe these to be correlated but I can't tell you how...