Next, given a training set, of M, unlabled examples,

Suppose we have a training set of m examples like that shown here.

So here are some examples, segmentations from the work of segmentation data set.

Because, M when trading set size, right, have three training examples.

label training set, with, let's say, a few label examples of cars and a few

Let's say we have a data set of m examples, so x1 through x(m), and let's say each of these examples is a real number.

A couple of examples.

lot of factoring examples.

Other examples of this.

A couple of examples.

And so our training set will now be only, maybe 60 of the data, and our cross validation set or our validation set will have some number of examples.

Let's say that we have an unlabeled training set of M examples, and each of these examples is going to be a feature in Rn so your training set could be, feature vectors from the last

And therefore fail to generalize the new examples not in the training set.

Examples

Examples

Examples

Examples

Just a couple of examples.

There are examples of problems,

All examples of models changing.

Some examples, well the airlines are the perfect examples of keeping customers.

Here are 3 examples of email.

Let's think of a couple examples.

In one of most classic examples,

Let's look at some examples of

There aren't enough examples of failures.

Filter Examples

Some examples.

Examples of posed variation from a single subject and examples of the shape variation between subjects.

In this Quran, We have set out all kinds of examples for people, yet most of them persist in denying the truth.

There is decades of research, examples and examples of cases like this, where individuals really, really believe.

And finally we also have a test set over here with M subscript test, being the number of test examples.

First for each of my training examples, I'm going to set this variable CI to be the index 1 through

Here are two examples of this principle.

So here are a couple of examples.

And there are lots of other examples.

We have great examples of that, unfortunately.

There are lots of more sophisticated examples.

And like all of the other examples,

We've seen some examples of that already.

And it's also true of other examples,

There were a couple of examples here.

So let's take a couple of examples.

So let's do a couple of examples.

Here are some examples of classification problems.