Since we talked about our neural network learning, let's briefly touch on the three different types of learning you should be aware of. They are supervised, unsupervised, and reinforcement.
Types of learning
Supervised learning
If you have a large test dataset that matches up with known results, then supervised learning might be a good choice for you. The neural network will process a dataset; compare its output against the known result, adjust, and repeat. Pretty simple, huh?
Unsupervised learning
If you don't have any test data, and it is possible to somehow derive a cost function from the behavior of the data, then unsupervised learning might be a good choice for you. The neural network will process a dataset, use the cost function to tell how much the error rate is, adjust the parameters, then repeat. All this while working in real time!
Reinforcement learning
Our final type of learning is reinforcement learning, better known in some circles as carrot-and-stick. The neural network will process a dataset, learn from the data, and if our error rate decreases, we get the carrot. If the error rate increases, we get the stick. Enough said, right?
