Workings of machine learning

ML is still an umbrella term—there are many different ways in which we can implement it, namely, K-means clustering, logistic regression, linear regression, support vector machines, and many more. In this book, we'll be mainly focusing on one type of machine learning, that is, artificial neural networks (ANNs).

ANN, or neural networks for short, are a set of techniques, some of which can be referred to as deep learning. It is a type of machine learning algorithm that is, at a very high-level, inspired by the structure of our biological nervous systems. By high-level, we mean that the algorithms are nowhere near to being the same. As a matter of fact, we barely understand how our nervous system learns in the first place. But even the part that was inspired by our nervous system, its structure, is still primitive. While your brain may have hundreds of different kinds of neurons arranged in a type of a web with over 100 trillion synapses, ANNs, so far, only have a handful of different kinds of neurons arranged in a layered formation, and have, at most, a few hundred million artificial synapses.

Machine learning algorithms, including ANNs, learn in the following two ways:

• Supervised learning: This method of learning allows the machine to learn by example. The computer is shown numerous input-output pairs, and it learns how to map input to output, even if it has never seen a certain input before. Since supervised learning systems require input and output to learn mappings, it's typically more difficult to collect data for these systems. If you'd like to train a supervised learning system to detect cats and dogs in photos, you'd need to have massive, hand-labeled datasets of images of cats and dogs and train the algorithm.
• Unsupervised learning: This method of learning allows the machine to learn entirely on its own. It's only shown a certain set of data, and tries to learn representations that fit the data, and can then represent new data that it has never seen before. Due to the fact that only input data is required, the method of data collection for unsupervised learning is typically easier. You'll see some examples toward the end of the book.

You can also combine these methods into a semi-supervised machine learning method, but it depends on the individual use case.