Linear separability is an important concept in neural networks. The idea is to check if you can separate points in an n-dimensional space using only n-1 dimensions. Lost it? Here's a simpler explanation.
Lets say you're on a number line. You take any two numbers. Now, there are two possibilities:
You choose two different numbers
You choose the same number
If you choose two different numbers, you can always find another number between them. This number "separates" the two numbers you chose.
So, you say that these two numbers are "linearly separable".
But, if both numbers are the same, you simply cannot separate them. They're the same. So, they're "linearly inseparable". (Not just linearly, they're aren't separable at all. You cannot separate something from itself)
On extending this idea to two dimensions, some more possibilities come into existence. Consider the following:
Here, we're like to seperate the point (1,1) from the other points. You can see that there exists a line that does this. In fact, there exist infinite such lines. So, these two "classes" of points are linearly separable. The first class consists of the point (1,1) and the other class has (0,1), (1,0) and (0,0).
Now consider this:
In this case, you just cannot use one single line to separate the two classes (one containing the black points and one containing the red points). So, they are linearly inseparable.
Extending the above example to three dimensions. You need a plane for separating the two classes.
The dashed plane separates the red point from the other blue points. So its linearly separable. If bottom right point on the opposite side was red too, it would become linearly inseparable .
Extending to n dimensions
Things go up to a lot of dimensions in neural networks. So to separate classes in n-dimensions, you need an n-1 dimensional "hyperplane".
So hopefully, you've understood how linear separability works. You'll be seeing this again and again in several other articles related to neural networks.