How to pronounce “dataset” in English

This dataset has a lot of strange confounds that you can pick up on.

This dataset doesn't encompass Wily Mo Pena.

So datasets are great.

and have the tagged dataset made anonymous and available so they can see the process that got us there.

in our dataset.

And they released this dataset.

With the newsgroup dataset, were any one of those something that a human looked at and said, aha, that's more interpretable, and that makes more sense?

Because your dataset happened to have this bias in it that you were not aware of.

So the test dataset is divided into halves.

The individual dataset, which is used to compute the precision, and the ensemble dataset, which is used to build the ensemble

So this dataset is just looking at home runs in 2009.

But this dataset tells us a very similar thing.

We have a third dataset.

Greg's dataset looking at the trajectory, flight time, and landing spot gets this data set for 2015.

is a high-frequency dataset.

This is a fabulous dataset, we've got thousands and thousands of business records from some middlemen in the trade operating over decades and it would several great dissertations in

Let's take public datasets and identify how we can more clearly map vectors and then express that information to health care

from massive datasets with real privacy guarantees for all the people's information that are in that data set.

of big datasets.

cross-country datasets that are ...

made available using Symphony's dataset.

But when you build a large enough dataset-- and currently, MobileODT is proud to have one of the largest datasets in the world on cervical cancer--

So we do have a dataset that we made available through Kaggle in a competition that Intel sponsored on type of cervix, which

And we needed a longer-term dataset in which to make decisions to know about the status of different fish populations.

I did not look in this dataset at people that derailed because they loved living in Boulder, and they wanted to stay in Boulder,

For each data point in our dataset, we drop a Gaussian on top of it.

So this is using to PITCHf/x dataset, and understanding the different pitch types, and understanding how these pitches move as a measurement

But this was an important little dataset to understand.

Now we can go to another dataset.

This is actually not the third dataset yet.

This is actually a big dataset for baseball.

And somebody asked about the butane dataset, and then he raised his hand.

She does a project called Missing Datasets.

The point is this is datasets that we can use to analyze pitching performance, the movement of the baseball.

But we had a problem of only having a limited dataset of fish populations and exploitation.

And if we want to query the density of the dataset at any given point-- let's say right here-- we sum up the contribution from all of the individual Gaussians

be good at a fundraiser because based upon what your accumulated dataset has

is also enlightening to them about the nature of the dataset that you have on hand.

Of the features you actually had in this dataset, is there is there one where, if you took it out, considerably worsened things?

Now, here's another dataset.

And then someone else asked another question about another dataset, and the conversation went along, and he shifted out the back.

Because we had this beautiful data privilege and this dataset from the stock market, and we had also access to the bank,

goes there, the dataset goes there, and your explanation goes there.

So here you see a lot of folders full of missing datasets.

She keeps a GitHub repository where she has these missing datasets.

How do you make it be a part of the datasets that you're-- the problem sets you're working on?

We merged these datasets.

So I just heard that the National Institutes of Health just published a huge dataset of MRI-based images.

So if you have a dataset, but the way of collecting the data changes, then the type of analysis,