The goal of training is to improve a model 
so that it can make high-quality estimations or predictions. 
Once trained, you can use a model in the real world like normal software.

Models don’t train themselves. 
They're trained using data plus two pieces of code, 
	the objective function 
	and 
	the optimizer. 
Let’s explore how these components work together 
to train a model to work well.

1. Objective Function : judge whether the model is doing a good job (estimating boot size well) or bad job (estimating boot size badly).
2. Optimizer :

1. Objective Function

What is Objective ?
The objective is what we want the model to be able to do. 
For example, the objective of our scenario is to be able to 
estimate a dog’s boot size based on their harness size.

So that a computer can understand our objective, 
we need to provide our goal as code snippet called an objective function 
(also known as cost function). 
Objective functions judge whether the model is doing a good job 
	(estimating boot size well) or bad job (estimating boot size badly). 
We'll cover objective functions in more depth in later learning material.

What is data ?
Data refers to the information 
that we provide to the model (also known as inputs). 
In our scenario, this is harness size.

Data also refers to information that the objective function might need. 
For example, 
if our objective function reports whether the model guessed the boot size correctly, 
it will need to know the correct boot size! 
This is why in our previous exercise, 
we provided both harness sizes and the correct answers to the training code.

We'll practice working with data in the next exercise.

2. Optimizer

The optimizer
During training, the model makes a prediction, 
and the objective function calculates how well it performed. 

The optimizer is code that then changes the model’s parameters 
so the model will do a better job next time.

How an optimizer does this is complex, 
and something we'll cover in later material. 
Don’t be intimidated, though; w
e don’t normally write our own optimizers, 
we use open-source frameworks where the hard work has been done for us.

It's important to keep in mind that the 
objective, 
data, 
and optimizer 
are simply a means to train the model. 

They are not needed once training is complete. 
It's also important to remember that 
training only changes the parameter values inside of a model; 
it doesn't change what kind of model is used.