I'm sweating, this is so computationally intensive

Actually, computationally, it would be impossible to create.

It is computationally very inefficient, so you have to basically go over the entire state space.

This is a reconstruction of Notre Dame Cathedral that was done entirely computationally from images scraped from Flickr.

And hopefully you are already are thinking about how you might actually implement this merge in a computationally efficient way.

So you can use that property in general, though you'll very seldom see something like this, because computationally it would take you forever to do it.

lot of features and you might up overfitting the training set, and it can also be computationally expensive, you know, to be working with that many features.

But in the future we're going to talk about a little bit more how do we think about how computationally intensive different ways of implementing a function, can be.

That's a mouthful but it turns out that this is the world's best way to go through big data sets, huge data sets in a way that was just computationally not possible before.

One of the reasons for this resurgence is that Neural Networks are computationally some what more expensive algorithm and so, it was only, you know, maybe somewhat more recently that computers became fast enough to really run large scale

It turns out linear algebra isn't just useful for linear regression models but these ideas of matrices and vectors will be useful for helping us to implement and actually get computationally efficient implementations for many later machines learning models as well.

So what we do is we send that bullet that packet of photons millions of times, and record again and again with very clever synchronization, and from the gigabytes of data, we computationally weave together to create those femto videos I showed you.

And the reason why they work so well is they're really easy to implement, they're computationally efficient in the sense that they really put the computational resources where they are needed the most, and they can deal with highly non monotonic and very complex posterior distribution that have many peaks.

But, for subtle reasons, that we will see later, it turns out to be much more computationally efficient to make predictions on all of the prices of all of your houses doing it the way on the left than the way on the right than if you were to write your own formula.

let me, let me just do Fibonacci of like 6, and your gonna see how many calls to Fibonacci it had to do so it had to do all of these calls, all of these calls, to fibonacci so it was actually very computationally intensive thing to do. but it, you know, the computer's are super fast, so you dont even notice it.