In a linear combination it is easy to create **arbitrarily long sequences** of pairs of weights (thetas) and input values (x). But first, just a quick recap of how a simple linear combination might look like.

score = theta_1 * x_1 + theta_2 * x_2 + theta_3 * x_3

Since each x value is multiplied with the theta, we can very easily **disable the variable by setting the theta to 0**. When working with real world data this procedure of setting the theta to 0 to disable it is commonly used in the practical implementation since it allows for a more optimized implementation.

In coming videos we are going to put these x values and thetas in vectors. Instead of having dynamically sized vectors we can then have vectors static in size, where some items in this vectors are set to 0. **Knowing the size of the vector beforehand** gives some really good benefits since you can use some tools from python, such as** sparse vectors and matrices**. These implementations of sparse vectors and matrices are **much more effective** and** uses much less RAM** (about 10 times as little) than the python lists which are composed of python objects. But in order to use these implementations from the** python library numpy** we need to set the size of the array before executing the program.