With artificial intelligence (AI) constantly advancing, and its popularity steadily rising, it’s easy to get caught in the confusing buzzwords like machine learning and deep learning. Terms like these can often be seen as overlapping and interchangeable. However, the truth is that deep learning is just a sub-field of machine learning.
Keeping this in mind, it’s important to first define what machine learning is in order to make a clear distinction between it and deep learning.
Machine learning (ML) is an implementation of Artificial Intelligence in which a computer is fed data, and it is supposed to “learn” and make choices based on that data. By doing this, a computer can start making choices without the need to be explicitly programmed to do so. Moreover, the “learning” of the computer can be supervised or unsupervised.
Supervised learning consists of a machine being fed data that is already labeled. This way, the machine learns from the data and a model that instructs it how to react to said training data.
Unsupervised learning consists of a machine being fed data that is unlabeled. The machine is left to its own devices to find patterns that may also surprise the person in charge of the machine.
Now that we have machine learning out of the way, let’s draw our attention to the basics of deep learning. In simple words, deep learning is just a mathematically complex version of machine learning.
Deep learning introduces complex neural networks, which are complicated layers of algorithms, that allow a machine to solve problems in a way that a person would. These neural networks are designed based on the human brain, where data is transferred between different nodes, which resemble neurons in people.
In comparison to ML models, deep learning models do not require as much supervision and guidance because they are programmed to decide for themselves whether they are making the right choice or not.