Now that we know what a neural network and neurons are, we should talk about what they do and how they do it. How does a neural network learn? Those of you with children already know the answer to this one. If you want your child to learn what a cat is, what do you do? You show them cats (pictures or real). You want your child to learn what a dog is? Show them dogs. A neural network is conceptually no different. It has a form of learning rule that will modify the incoming weights from the input layer, process them through the hidden layers, put them through an activation function, and hopefully will be able to identify, in our case, cats and dogs. And, if done correctly, the cat does not become a dog!

One of the most common learning rules with neural networks is what is known as the delta rule. This is a supervised rule that is invoked each time the network is presented with another learning pattern. Each time this happens it is called a cycle or epoch. The invocation of the rule will happen each time that input pattern goes through one or more forward propagation layers, and then through one or more backward propagation layers.

More simply put, when a neural network is presented with an image it tries to determine what the answer might be. The difference between the correct answer and our guess is the error or error rate. Our objective is that the error rate gets either minimized or maximized. In the case of minimization, we need the error rate to be as close to 0 as possible for each guess. The closer we are to 0, the closer we are to success.

As we progress, we undertake what is termed a gradient descent, meaning we continue along toward what is called the global minimum, our lowest possible error, which hopefully is paramount to success. We descend toward the global minimum.

Once the network itself is trained, and you are happy, the training cycle can be put to bed and you can move on to the testing cycle. During the testing cycle, only the forward propagation layer is used. The output of this process results in the model that will be used for further analysis. Again, no back propagation occurs during testing.