深度学习学习笔记——C2W2-3——指数加权平均

Exponentially Weighted Averages指数加权平均

I want to show you a few optimization algorithms. They are faster than gradient descent. In order to understand those algorithms, you need to be able they use something called exponentially weighted averages. Also called exponentially weighted moving averages in statistics.

Let's first talk about that, and then we'll use this to build up to more sophisticated optimization algorithms. So, even though I now live in the United States, I was born in London. So, for this example I got the daily temperature from London from last year. So, on January 1, temperature was 40 degrees Fahrenheit. Now, I know most of the world uses a Celsius system, but I guess I live in United States which uses Fahrenheit. So that's four degrees Celsius. And on January 2, it was nine degrees Celsius and so on. And then about halfway through the year, a year has 365 days so, that would be, sometime day number 180 will be sometime in late May, I guess. It was 60 degrees Fahrenheit which is 15 degrees Celsius, and so on. So, it start to get warmer, towards summer and it was colder in January. So, you plot the data you end up with this. Where day one being sometime in January, that you know, being the, beginning of summer, and that's the end of the year, kind of late December. So, this would be January, January 1, is the middle of the year approaching summer, and this would b